Error Detection in Knowledge Graphs

Knowledge graphs (KGs) have been widely applied as an efficient tool in storing information in the digital age. They always contain a considerable number of errors and could significantly affect downstream tasks. To tackle this issue, developing generalizable error detection algorithms on KGs is needed. However, it is still challenging due to the unique data characteristics of KGs. In this talk, I will present my work that could learn both the sequential information within the triples and contextual information for KG error detection

Niclosamide enhances abiraterone treatment via inhibition of androgen receptor variants in castration resistant prostate cancer.

Considerable evidence from both clinical and experimental studies suggests that androgen receptor variants, particularly androgen receptor variant 7 (AR-V7), are critical in the induction of resistance to enzalutamide and abiraterone. In this study, we investigated the role of AR-V7 in the cross-resistance of enzalutamide and abiraterone and examined if inhibition of AR-V7 can improve abiraterone treatment response. We found that enzalutamide-resistant cells are cross-resistant to abiraterone, and that AR-V7 confers resistance to abiraterone. Knock down of AR-V7 by siRNA in abiraterone resistant CWR22Rv1 and C4-2B MDVR cells restored their sensitivity to abiraterone, indicating that AR-V7 is involved in abiraterone resistance. Abiraterone resistant prostate cancer cells generated by chronic treatment with abiraterone showed enhanced AR-V7 protein expression. Niclosamide, an FDA-approved antihelminthic drug that has been previously identified as a potent inhibitor of AR-V7, re-sensitizes resistant cells to abiraterone treatment in vitro and in vivo. In summary, this preclinical study suggests that overexpression of AR-V7 contributes to resistance to abiraterone, and supports the development of combination of abiraterone with niclosamide as a potential treatment for advanced castration resistant prostate cancer

Explaining Dynamic Graph Neural Networks via Relevance Back-propagation

Graph Neural Networks (GNNs) have shown remarkable effectiveness in capturing abundant information in graph-structured data. However, the black-box nature of GNNs hinders users from understanding and trusting the models, thus leading to difficulties in their applications. While recent years witness the prosperity of the studies on explaining GNNs, most of them focus on static graphs, leaving the explanation of dynamic GNNs nearly unexplored. It is challenging to explain dynamic GNNs, due to their unique characteristic of time-varying graph structures. Directly using existing models designed for static graphs on dynamic graphs is not feasible because they ignore temporal dependencies among the snapshots. In this work, we propose DGExplainer to provide reliable explanation on dynamic GNNs. DGExplainer redistributes the output activation score of a dynamic GNN to the relevances of the neurons of its previous layer, which iterates until the relevance scores of the input neuron are obtained. We conduct quantitative and qualitative experiments on real-world datasets to demonstrate the effectiveness of the proposed framework for identifying important nodes for link prediction and node regression for dynamic GNNs

Association between serum uric acid and phase angle in patients with type 2 diabetes mellitus: A cross-sectional study

BackgroundThe purpose of this analysis was to investigate the associations between serum uric acid and phase angle in patients with type 2 diabetes mellitus.MethodsIn this retrospective cross-sectional study, we included 200 type 2 diabetes mellitus (T2DM) patients treated during 2018–2019 at Zhongda Hospital Southeast University. Phase angle (PhA) and other body composition indicators were measured by bioelectrical impedance analysis (BIA). All patients underwent routine clinical examinations on the day of hospitalization, and the basic information and clinical symptoms of these patients were recorded.ResultsSerum uric acid (UA) was significantly associated with PhA (p <0.001). Overall, in the crude model and minor, all adjusted models (crude model, Models I–II), the phase angle increased as the tertiles of serum uric acid increased. In the minor adjusted model (Model I, adjustment for age and duration) fully adjusted model (Model II, adjustment for age, duration, Lpa, BMI, and WHR), the adjusted β for participants in tertiles of serum uric acid were 0.26 (95% CI: 0.05–0.46) and 0.32 (95% CI: 0.11–0.54), respectively, compared with those in the lowest tertile 1.ConclusionThere was a nonlinear relationship between serum uric acid and PhA in T2DM patients, and the phase angle increased as uric acid increased within a certain range, and this effect disappeared when uric acid exceeded a certain value

Radiomics-based left ventricular ejection fraction prediction: a feasibility study

Objective·To assess the feasibility of using 3D imaging features extracted from cardiac magnetic resonance (CMR) short-axis cine images to predict left ventricular ejection fraction (LVEF).Methods·A total of 100 left ventricular hypertrophy (LVH) patients who visited the Department of Cardiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine from January 2018 to December 2021, as well as 100 healthy control (HC) subjects during the same period, were included. All subjects completed CMR examinations under the supervision of experienced cardiologists and radiologists. The endocardial and epicardial contours were then manually delineated by cardiologists. Measurements of cardiac function and morphology were completed and data was recorded, including LVEF, left ventricular end-diastolic volume (LVEDV), and left ventricular end-diastolic mass (LVEDM). Myocardial 3D radiomic features of CMR-cine sequences were extracted by the Pyradiomics package, and selected and sorted by using correlation coefficient and K-best method. The LVEF prediction was performed with linear regression (LR), random forest (RF) and gradient boost (GB) methods. Results were also compared with LVEF prediction based on clinical information and CMR parameters.Results·In terms of clinical indicators, there were significant differences between the LVH and HC groups, such as LVEDV and LVEDM (all P<0.05); after extracting 3D radiomic features, the top 10 features were selected for further analysis. LR regression model, GB regression model and RF regression model were constructed for predicting the LVEF, and RF regression models showed the best results with seven features, in which the mean absolute error (MAE) was 0.066±0.002. Further comparison results showed that the model using radiomic information with CMR parameters (MAE=0.056±0.001) had the best performance and it was significantly better than the model using radiomic features (MAE=0.066±0.002) or CMR parameters (MAE=0.060±0.001) alone (both P<0.05).Conclusion·The use of radiomic features for LVEF prediction has certain feasibility, and combining radiomic features with CMR parameters can further improve the prediction accuracy of the model

Effect of somatic symptoms, anxiety and depression on clinical prognosis in patients with chronic heart failure

Objective·To explore the association of somatizatic symptoms, anxiety and depression with clinical prognosis in the patients with chronic heart failure (CHF).Methods·The patients with CHF who visited the Department of Cardiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine from January 2018 to December 2021 were included. Demographic data and clinical features of the patients were collected. The Self-reported Somatic Symptom Scale of China (SSS-CN), the Patient Health Questionnaire-9 (PHQ-9), and the Generalized Anxiety Disorder-7 (GAD-7) were used to evaluate the patients′ conditions. Telephone follow-up was conducted at the 12th month after the first visit, and the specific information of the patients′ end-point events (including death, re-hospitalization, causes of death and re-hospitalization) was collected. Survival curve and Cox regression analysis were used to evaluate the clinical prognosis of the patients.Results·A total of 195 patients were included. The SSS-CN scores in CHF patients were different between the two genders, among the different heart rate groups and the different cardiac function grades of New York Heart Association (NYHA), also between the patients with anxiety/depression or not (all P<0.05). Survival curve analysis showed that overall survival rate of patients in the moderate-severe somatic symptoms group was lower than that of the patients in the normal-mild group (Log rank P=0.020). Cox regression analysis showed that compared with the normal-mild group, the patients in the moderate-severe somatic symptoms group had a higher risk of all-cause death [hazard ratio (HR)=2.797, 95%CI 1.135－6.890]; the CHF patients with depressive symptoms had a higher risk of all-cause death (HR=2.883, 95%CI 1.150－6.984). Compared with the normal-mild group, the patients with moderate-severe somatic symptoms had a higher risk of cardiovascular death (HR=2.784, 95%CI 1.073－7.226). The CHF patients with depressive symptoms had a higher risk of cardiovascular death (HR=2.823, 95%CI 1.087－7.330). There were no statistically differences in anxiety, depression, somatization symptoms and their severity between all-cause hospitalization and hospitalization due to CHF.Conclusion·The moderate-severe somatic symptoms and depression are the risk factors of all-cause death and cardiovascular death in the patients with CHF

Phenotypic heterogeneity and evolution of melanoma cells associated with targeted therapy resistance

Phenotypic plasticity is associated with non-genetic drug tolerance in several cancers. Such plasticity can arise from chromatin remodeling, transcriptomic reprogramming, and/or protein signaling rewiring, and is characterized as a cell state transition in response to molecular or physical perturbations. This, in turn, can confound interpretations of drug responses and resistance development. Using BRAF-mutant melanoma cell lines as the prototype, we report on a joint theoretical and experimental investigation of the cell-state transition dynamics associated with BRAF inhibitor drug tolerance. Thermodynamically motivated surprisal analysis of transcriptome data was used to treat the cell population as an entropy maximizing system under the influence of time-dependent constraints. This permits the extraction of an epigenetic potential landscape for drug-induced phenotypic evolution. Single-cell flow cytometry data of the same system were modeled with a modified Fokker-Planck-type kinetic model. The two approaches yield a consistent picture that accounts for the phenotypic heterogeneity observed over the course of drug tolerance development. The results reveal that, in certain plastic cancers, the population heterogeneity and evolution of cell phenotypes may be understood by accounting for the competing interactions of the epigenetic potential landscape and state-dependent cell proliferation. Accounting for such competition permits accurate, experimentally verifiable predictions that can potentially guide the design of effective treatment strategies

Essays on the optimal level of content protections

This dissertation consists of three essays on the domestic content protection schemes (CPSs) which have been widely used as devices to protect intermediate goods producers. The first essay examines the optimal level of content requirement when the objective is to maximize the joint welfare of several political groups as defined by the weighted sum of consumer surplus, the profit of domestic firms, and domestic employment expressed as the total wage payment. Our contributions here are, among other things, to identify (a) relationships between the level of the optimal domestic content requirement and the ratio of input prices; (b) relationships between the level of the optimal domestic content requirement and the total number of domestic firms competing in the domestic market; (c) relationships between the level of the optimal domestic content requirement and the total number of foreign subsidiaries competing in the domestic market. We also show that the optimal level of domestic content requirement depends on how content protection schemes are defined. The second essay extends the work of the first chapter by dealing with a model that assumes symmetric access to inputs. We focus on the effects of content schemes upon domestic employment in order to provide labor organizations and other pro-workers\u27 constituencies with a clear picture of relationships between the optimal local content protection and (a) the capital-labor ratio in the production function; (b) the ratio of domestic input cost to the imported input cost; and (c) the total number of domestic firms in the market; (d) the total number of foreign subsidiaries in the market. We suggest the relative importance of the various forces involved in determining the optimal content protection. Our analysis sheds light on why the optimal domestic content requirement may vary when content schemes are imposed on different nations or on different industries. Studies on multimarket oligopolies have become more widespread since Bulow, Geanakoplos and Klemperer (1985). The third essay identifies the strategic externalities of CPSs and examines their economic implications. Strategic externalities are said to exist when decisions made on origin rules to serve a government policy in one market affect optimal decisions and outcomes in other markets

Multi-Label Classification Based on Low Rank Representation for Image Annotation

Annotating remote sensing images is a challenging task for its labor demanding annotation process and requirement of expert knowledge, especially when images can be annotated with multiple semantic concepts (or labels). To automatically annotate these multi-label images, we introduce an approach called Multi-Label Classification based on Low Rank Representation (MLC-LRR). MLC-LRR firstly utilizes low rank representation in the feature space of images to compute the low rank constrained coefficient matrix, then it adapts the coefficient matrix to define a feature-based graph and to capture the global relationships between images. Next, it utilizes low rank representation in the label space of labeled images to construct a semantic graph. Finally, these two graphs are exploited to train a graph-based multi-label classifier. To validate the performance of MLC-LRR against other related graph-based multi-label methods in annotating images, we conduct experiments on a public available multi-label remote sensing images (Land Cover). We perform additional experiments on five real-world multi-label image datasets to further investigate the performance of MLC-LRR. Empirical study demonstrates that MLC-LRR achieves better performance on annotating images than these comparing methods across various evaluation criteria; it also can effectively exploit global structure and label correlations of multi-label images