Parallelizing Description Logic Reasoning

Description Logic has become one of the primary knowledge representation and reasoning methodologies during the last twenty years. A lot of areas are benefiting from description logic based technologies. Description logic reasoning algorithms and a number of optimization techniques for them play an important role and have been intensively researched. However, few of them have been systematically investigated in a concurrency context in spite of multi-processor computing facilities growing up. Meanwhile, semantic web, an application domain of description logic, is producing vast knowledge data on the Internet, which needs to be dealt with by using scalable solutions. This situation requires description logic reasoners to be endowed with reasoning scalability. This research introduced concurrent computing in two aspects: classification, and tableau-based description logic reasoning. Classification is a core description logic reasoning service. Over more than two decades many research efforts have been devoted to optimizing classification. Those classification optimization algorithms have shown their pragmatic effectiveness for sequential processing. However, as concurrent computing becomes widely available, new classification algorithms that are well suited to parallelization need to be developed. This need is further supported by the observation that most available OWL reasoners, which are usually based on tableau reasoning, can only utilize a single processor. Such an inadequacy often leads users working in ontology development to frustration, especially if their ontologies are complex and require long processing times. Classification service finds out all named concept subsumption relationships entailed in a knowledge base. Each subsumption test enrolls two concepts and is independent of the others. At most n^2 subsumption tests are needed for a knowledge base which contains n concepts. As the first contribution of this research, we developed an algorithm and a corresponding architecture showing that reasoning scalability can be gained by using concurrent computing. Further, this research investigated how concurrent computing can increase performance of tableau-based description logic reasoning algorithms. Tableau-based description logic reasoning decides a problem by constructing an AND-OR tree. Before this research, some research has shown the effectiveness of parallelizing processing disjunction branches of a tableau expansion tree. Our research has shown how reasoning scalability can be gained by processing conjunction branches of a tableau expansion tree. In addition, this research developed an algorithm, merge classification, that uses a divide and conquer strategy for parallelizing classification. This method applies concurrent computing to the more efficient classification algorithm, top-search & bottom-search, which has been adopted as a standard procedure for classification. Reasoning scalability can be observed in a number of real world cases by using this algorithm

The Impact of Subsea Gas Releases and Resulting Gas Plumes Using Computational Fluid Dynamics

PresentationA Computational Fluid Dynamics (CFD) model was developed to describe the behavior of a subsea gas release and the subsequent rising gas plume. Four numerical approaches were assessed for their suitability to capture the characteristic behaviors in a rising gas plume by comparing the CFD results with experimental data obtained from an underwater gas release experiment carried out in a 10 m depth towing tank basin. The k-ε turbulence model was found to be unsatisfactory in capturing random wandering behavior of the subsea gas plume due to the inherent Reynolds-Averaged Navier-Stokes (RANS) nature of the approach. The result is an over-prediction of the plume central line velocity and an under-prediction of the plume width as there was no mechanism to distribute and dissipate the high momentum gained during the initial gas release phase. The results obtained using the Large Eddy Simulation (LES) approach show the inherently random wandering behavior of the plume is successfully captured and both the centerline velocity and the velocity profile are in much better agreement with the experimental data

Suppressing the MLK3 promotes glutamine metabolism: mechanism and implications in progression of colon cancer

This study was designed to explore the potential role of mixed-lineage protein kinase 3 (MLK3) in colorectal cancer (CRC) progression and its relationship with glutamine metabolism. The immunohistochemical staining results of MLK3 were primarily collected through 100 CRC patients. Wound healing and transwell assays were used to detect migration ability of CRC cells by transfecting cells with siMlk3. Gene set variation analysis (GSVA) and Spearman’s rank correlation coefficient were used as bioinformatics tools to explore the signaling pathways related to MLK3. Western blotting was performed to analyze the downstream of glutamine metabolism. The results suggested an increased expression of MLK3 in CRC tissues, which was related to adverse clinicopathological characteristics in those CRC patients. Knockdown of MLK3 inhibited the proliferative and migratory potential of CRCs. Bioinformatics analysis confirmed the relationship between MLK3 expression and cancer malignancy related signaling pathways. CRC cell lines transfected with siMlk3 suppressed glutamine metabolism by downregulating the glutamine transporter alanine-serine-cysteine transporter 2 (ASCT2). These results suggested the vital role of MLK3 in CRC progression, which may be related to the suppression of glutamine transporter, namely alanine, serine, cysteine transporter 2 (ASCT2)

Expression of HER2 in high-grade urothelial carcinoma based on Chinese expert consensus and the clinical effects of disitamab vedotin-tislelizumab combination therapy in the treatment of advanced patients

Background: A vast number of researchers have discovered high levels of human epidermal growth factor receptor-2 (HER2) expression in urothelial carcinoma (UC), but they do not use a uniform scoring system. Based on the 2021 edition of clinical pathological expert consensus on HER-2 testing in UC in China, we investigated the expression level and clinical significance of HER2 in high-grade UC. Furthermore, we looked at the prognosis of patients with locally advanced/metastatic UC after combining HER2 targeting antibody-drug conjugates (ADC) medication disitamab vedotin (DV) with programmed cell death protein 1 (PD-1) inhibitor tislelizumab.Patients and methods: From 2019 to 2022, we collected paraffin specimens of UC from the Department of Urology at the Provincial Hospital Affiliated to Shandong First Medical University. HER2 expression-related factors were investigated. Patients with advanced UC who have failed systemic chemotherapy at least once and had received immune checkpoint inhibitor (ICI) medication during second-line treatment were selected and treated with DV in combination with tislelizumab. We assessed the therapy’s efficacy and safety.Results: 185 patients with high-grade UC were included in this investigation. 127 patients (68.7%) were HER2 positive (IHC 2+/3+) according to the 2021 Clinical pathological expert consensus on HER2 testing in UC in China. The clinical stage of UC differed statistically significantly between the HER2-and HER2+ groups (p = 0.019). Sixteen advanced UC patients were treated with DV and tislelizumab for a median of 14 months. The disease control rate was 87.5%, while the objective response rate (ORR) was 62.5%. The ORR of HER2+ individuals was higher than that of HER2-individuals (70.0% vs. 50.0%). The median progression-free survival or overall survival was not reached. In this study, the incidence of treatment-related adverse events was 68.8% (11/16), with all of them being grade 1 or 2 adverse reactions.Conclusion: HER2 protein expressed at a high percentage in UC, and 68.7% patients expressed HER2 positive (IHC 2+/3+). HER2+ expression is positively correlated with higher clinical stage of UC. HER2 targeted ADC drug disitamab vedotin combining with PD-1 inhibitor tislelizumab has shown efficacy, safety and controllable adverse reactions in the treatment of advanced UC

Flexible Informed Trees (FIT*): Adaptive Batch-Size Approach for Informed Sampling-Based Planner

In modern approaches to path planning and robot motion planning, anytime almost-surely asymptotically optimal planners dominate the benchmark of sample-based planners. A notable example is Batch Informed Trees (BIT*), where planners iteratively determine paths to groups of vertices within the exploration area. However, maintaining a consistent batch size is crucial for initial pathfinding and optimal performance, relying on effective task allocation. This paper introduces Flexible Informed Tree (FIT*), a novel planner integrating an adaptive batch-size method to enhance task scheduling in various environments. FIT* employs a flexible approach in adjusting batch sizes dynamically based on the inherent complexity of the planning domain and the current n-dimensional hyperellipsoid of the system. By constantly optimizing batch sizes, FIT* achieves improved computational efficiency and scalability while maintaining solution quality. This adaptive batch-size method significantly enhances the planner's ability to handle diverse and evolving problem domains. FIT* outperforms existing single-query, sampling-based planners on the tested problems in R^2 to R^8, and was demonstrated in real-world environments with KI-Fabrik/DARKO-Project Europe.Comment: 8 pages,6 figure

A hemodynamic analysis of energy loss in abdominal aortic aneurysm using three-dimension idealized model

Objective: The aim of this study is to perform specific hemodynamic simulations of idealized abdominal aortic aneurysm (AAA) models with different diameters, curvatures and eccentricities and evaluate the risk of thrombosis and aneurysm rupture.Methods: Nine idealized AAA models with different diameters (3 cm or 5 cm), curvatures (0° or 30°) and eccentricities (centered on or tangent to the aorta), as well as a normal model, were constructed using commercial software (Solidworks; Dassault Systemes S.A, Suresnes, France). Hemodynamic simulations were conducted with the same time-varying volumetric flow rate extracted from the literature and 3-element Windkessel model (3 EWM) boundary conditions were applied at the aortic outlet. Several hemodynamic parameters such as time-averaged wall shear stress (TAWSS), oscillatory shear index (OSI), relative residence time (RRT), endothelial cell activation potential (ECAP) and energy loss (EL) were obtained to evaluate the risk of thrombosis and aneurysm rupture under different conditions.Results: Simulation results showed that the proportion of low TAWSS region and high OSI region increases with the rising of aneurysm diameter, whereas decreases in the curvature and eccentric models of the corresponding diameters, with the 5 cm normal model having the largest low TAWSS region (68.5%) and high OSI region (40%). Similar to the results of TAWSS and OSI, the high ECAP and high RRT areas were largest in the 5 cm normal model, with the highest wall-averaged value (RRT: 5.18 s, ECAP: 4.36 Pa−1). Differently, the increase of aneurysm diameter, curvature, and eccentricity all lead to the increase of mean flow EL and turbulent EL, such that the highest mean flow EL (0.82 W · 10−3) and turbulent EL (1.72 W · 10−3) were observed in the eccentric 5 cm model with the bending angle of 30°.Conclusion: Collectively, increases in aneurysm diameter, curvature, and eccentricity all raise mean flow EL and turbulent flow EL, which may aggravate the damage and disturbance of flow in aneurysm. In addition, it can be inferred by conventional parameters (TAWSS, OSI, RRT and ECAP) that the increase of aneurysm diameter may raise the risk of thrombosis, whereas the curvature and eccentricity appeared to have a protective effect against thrombosis

Identification of hub genes associated with hepatitis B virus-related hepatocellular cancer using weighted gene co-expression network analysis and protein-protein interaction network analysis

Background. Chronic hepatitis B virus (HBV) infection is the main pathogen of hepatocellular carcinoma. However, the mechanisms of HBV-related hepatocellular carcinoma (HCC) progression are practically unknown. Materials and Methods. The results of RNA-sequence and clinical data for GSE121248 and GSE17548 were accessed from the Gene Expression Omnibus data library. We screened Sangerbox 3.0 for differentially expressed genes (DEGs). The weighted gene co-expression network analysis (WGCNA) was employed to select core modules and hub genes, and protein-protein interaction network module analysis also played a significant part in it. Validation was performed using RNA-sequence data of cancer and normal tissues of HBV-related HCC patients in the cancer genome atlas-liver hepatocellular cancer database (TCGA-LIHC). Results. 787 DEGs were identified from GSE121248 and 772 DEGs were identified from GSE17548. WGCNA analysis indicated that black modules (99 genes) and grey modules (105 genes) were significantly associated with HBV-related HCC. Gene ontology analysis found that there is a direct correlation between DEGs and the regulation of cell movement and adhesion; the internal components and external packaging structure of plasma membrane; signaling receptor binding, calcium ion binding, etc. Kyoto Encyclopedia of Genes and Genomes pathway analysis found out the association between cytokine receptors, cytokine-cytokine receptor interactions, and viral protein interactions with cytokines were important and HBV-related HCC. Finally, we further validated 6 key genes including C7, EGR1, EGR3, FOS, FOSB, and prostaglandin-endoperoxide synthase 2 by using the TCGALIHC. Conclusions. We identified 6 hub genes as candidate biomarkers for HBV-related HCC. These hub genes may act as an essential part of HBV-related HCC progression

Prediction of hyperuricemia in people taking low-dose aspirin using a machine learning algorithm: a cross-sectional study of the National Health and Nutrition Examination Survey

Background: Hyperuricemia is a serious health problem related to not only gout but also cardiovascular diseases (CVDs). Low-dose aspirin was reported to inhibit uric acid excretion, which leads to hyperuricemia. To decrease hyperuricemia-related CVD, this study aimed to identify the risk of hyperuricemia in people taking aspirin.Method: The original data of this cross-sectional study were obtained from the National Health and Nutrition Examination Survey between 2011 and 2018. Participants who filled in the “Preventive Aspirin Use” questionnaire with a positive answer were included in the analysis. Six machine learning algorithms were screened, and eXtreme Gradient Boosting (XGBoost) was employed to establish a model to predict the risk of hyperuricemia.Results: A total of 805 participants were enrolled in the final analysis, of which 190 participants had hyperuricemia. The participants were divided into a training set and testing set at a ratio of 8:2. The area under the curve for the training set was 0.864 and for the testing set was 0.811. The SHapley Additive exPlanations (SHAP) method was used to evaluate the performances of the modeling. Based on the SHAP results, the feature ranking interpretation showed that the estimated glomerular filtration rate, body mass index, and waist circumference were the three most important features for hyperuricemia in individuals taking aspirin. In addition, triglyceride, hypertension, total cholesterol, high-density lipoprotein, low-density lipoprotein, age, race, and smoking were also correlated with the development of hyperuricemia.Conclusion: A predictive model established by XGBoost algorithms can potentially help clinicians make an early detection of hyperuricemia risk in people taking low-dose aspirin