Global Exponential Stability of Learning-Based Fuzzy Networks on Time Scales

We investigate a class of fuzzy neural networks with Hebbian-type unsupervised learning on time scales. By using Lyapunov functional method, some new sufficient conditions are derived to ensure learning dynamics and exponential stability of fuzzy networks on time scales. Our results are general and can include continuous-time learning-based fuzzy networks and corresponding discrete-time analogues. Moreover, our results reveal some new learning behavior of fuzzy synapses on time scales which are seldom discussed in the literature

MuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale

Among the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that the forward pass iteratively reduces a graph-regularized energy function of interest. In this way, node embeddings produced at the output layer dually serve as both predictive features for solving downstream tasks (e.g., node classification) and energy function minimizers that inherit desirable inductive biases and interpretability. However, scaling GNN architectures constructed in this way remains challenging, in part because the convergence of the forward pass may involve models with considerable depth. To tackle this limitation, we propose a sampling-based energy function and scalable GNN layers that iteratively reduce it, guided by convergence guarantees in certain settings. We also instantiate a full GNN architecture based on these designs, and the model achieves competitive accuracy and scalability when applied to the largest publicly-available node classification benchmark exceeding 1TB in size

DGI: Easy and Efficient Inference for GNNs

While many systems have been developed to train Graph Neural Networks (GNNs), efficient model inference and evaluation remain to be addressed. For instance, using the widely adopted node-wise approach, model evaluation can account for up to 94% of the time in the end-to-end training process due to neighbor explosion, which means that a node accesses its multi-hop neighbors. On the other hand, layer-wise inference avoids the neighbor explosion problem by conducting inference layer by layer such that the nodes only need their one-hop neighbors in each layer. However, implementing layer-wise inference requires substantial engineering efforts because users need to manually decompose a GNN model into layers for computation and split workload into batches to fit into device memory. In this paper, we develop Deep Graph Inference (DGI) -- a system for easy and efficient GNN model inference, which automatically translates the training code of a GNN model for layer-wise execution. DGI is general for various GNN models and different kinds of inference requests, and supports out-of-core execution on large graphs that cannot fit in CPU memory. Experimental results show that DGI consistently outperforms layer-wise inference across different datasets and hardware settings, and the speedup can be over 1,000x.Comment: 10 pages, 10 figure

OR-051 Exploration of Potential Integrated Biomarkers for Sports Monitoring Based on Metabolic Profiling

Objective Metabolomic analysis is extensively applied to identify sensitive and specific biomarkers capable of reflecting pathological processes and physical responses or adaptations. Exercise training leads to profound metabolic changes, manifested as detectable alterations of metabolite levels and significant perturbations of metabolic pathways in sera, urine, and rarely, in saliva. Several metabolites have been exploited as biomarkers for generally evaluating physical states in almost all sports. However, alterations of metabolic profile caused by specific sports would be heterogeneous. Thus, developments of new techniques are eagerly required to identify characteristic metabolites as unique biomarkers for specifically accessing training stimulus and sports performances. In the present work, we conducted both metabolic profiling and a binary logistic regression model (BRM) of biological fluids derived from rowing ergometer test with the following aims: 1) to examine changes of metabolite profiles and identify characteristic metabolites in the samples of sera, urine, and saliva; 2) to screen out potential integrated biomarkers for sports-specific monitoring. Methods A total of 11 rowers (6 male, 5 female; aged 15±1 years; 4±2 years rowing training) underwent an indoor 6000m rowing ergometer test. Samples of sera, urine and saliva were collected before and immediately after the test. 1D 1H NMR spectra were recorded with a Bruker Avance III 650 MHz NMR spectrometer. NMR spectra were processed and aligned, resonances of metabolites were assigned and confirmed, and metabolite levels were calculated based on NMR integrals. Multivariate statistical analysis was carried out using partial least-squares discrimination analysis (PLS-DA) to distinguish metabolic profiles between the groups. The validated PLS-DA model gave the variable importance in the projection (VIP) for a given metabolite. Moreover, inter-group comparisons of metabolite levels were quantitatively conducted using the paired-sample t-test. Then, we identified characteristic metabolites with VIP>1 in PLS-DA and p<0.05 in t-test. Furthermore, we screened out potential biomarkers based on the characteristic metabolites identified from the three types of biological fluids using the BRM (stepwise). Results The rowing training induced profound changes of metabolic profiles in serum and saliva samples rather than in urine samples. Totally, 44 metabolites were assigned in which 19, 20, and 19 metabolites were identified from serum, urine and saliva samples, respectively. Seven metabolites were shared by the three types of samples. Moreover, five characteristic metabolites (pyruvate, lactate, succinate, N-acetyl-L-cysteine, and acetone) were identified from the serum samples. The elevated levels of pyruvate, lactate and succinate suggested that, the rowing training evidently promoted both oxidative phosphorylation and glycolysis pathways. Furthermore, three characteristic metabolites (tyrosine, formate, and methanol) were identified from the saliva samples. Given that tyrosine is the precursor of dopamine, the increased level of salivary tyrosine in all rowers experiencing the test, suggesting that salivary tyrosine could be explored as a potential indicator closely related to nervous fatigue in the test. On the other hand, PLS-DA did not show observable distinction of metabolic profiles between the urine samples before and immediately after the test. Moreover, 20 urinary metabolites did not display detectable altered levels. We then established the BRM with the identified characteristic metabolites, from which we selected one optimal regression model based on serum pyruvate and salivary tyrosine (adjusted R square was 0.935, P<0.001), indicating that the two selected metabolites would efficiently reflect the metabolic alterations in the test. Conclusions As far as the 6000m rowing ergometer test is concerned, serum samples could be a preferred resource for assessing the changes of energy metabolism in the test, while urine samples might have a relatively lower sensitivity to exercise-induced metabolic responses. Even though metabolite levels in saliva samples are generally lower than those in serum and urine samples, some salivary metabolites potentially have higher sensitivities to exercise-induced metabolic responses. Thus, the integration of multiple biomarkers identified from different type of species could potentially provide more sensitive and specific manners to monitor physical states in sports and exercise. This work may be of benefit to the exploration of integrated biomarkers for sports-specific monitoring

Urban Growth Modeling and Future Scenario Projection Using Cellular Automata (CA) Models and the R Package Optimx

Cellular automata (CA) is a spatially explicit modeling tool that has been shown to be effective in simulating urban growth dynamics and in projecting future scenarios across scales. At the core of urban CA models are transition rules that define land transformation from non-urban to urban. Our objective is to compare the urban growth simulation and prediction abilities of different metaheuristics included in the R package optimx. We applied five metaheuristics in optimx to near-optimally parameterize CA transition rules and construct CA models for urban simulation. One advantage of metaheuristics is their ability to optimize complexly constrained computational problems, yielding objective parameterization with strong predictive power. From these five models, we selected conjugate gradient-based CA (CG-CA) and spectral projected gradient-based CA (SPG-CA) to simulate the 2005-2015 urban growth and to project future scenarios to 2035 with four strategies for Su-Xi-Chang Agglomeration in China. The two CA models produced about 86% overall accuracy with standard Kappa coefficient above 69%, indicating their good ability to capture urban growth dynamics. Four alternative scenarios out to the year 2035 were constructed considering the overall effect of all candidate influencing factors and the enhanced effects of county centers, road networks and population density. These scenarios can provide insight into future urban patterns resulting from today's urban planning and infrastructure, and can inform future development strategies for sustainable cities. Our proposed metaheuristic CA models are also applicable in modeling land-use and urban growth in other rapidly developing areas

Urban expansion simulation and scenario prediction using cellular automata: comparison between individual and multiple influencing factors

Quantifying the contribution of driving factors is crucial to urban expansion modeling based on cellular automata (CA). The objective of this study is to compare individual-factor-based (IFB) models and multi-factor-based (MFB) models as well as examine the impacts of each factor on future urban scenarios. We quantified the contribution of driving factors using a generalized additive model (GAM), and calibrated six IFB-DE-CA models and fifteen MFB-DE-CA models using a differential evolution (DE) algorithm. The six IFB-DE-CA models and five MFB-DE-CA models were selected to simulate the 2005–2015 urban expansion of Hangzhou, China, and all IFB-DE-CA models were applied to project future urban scenarios out to the year 2030. Our results show that terrain (DEM) and population density (POP) are the two most influential factors affecting urban expansion of Hangzhou, indicating the dominance of biophysical and demographic drivers. All DE-CA models produced defensible simulations for 2015, with overall accuracy exceeding 89%. The IFB-DE-CA models based on DEM and POP outperformed some MFB-DE-CA models, suggesting that multiple factors are not necessarily more effective than a single factor in simulating present urban patterns. The future scenarios produced by the IFB-DE-CA models are substantially shaped by the corresponding factors. These scenarios can inform urban modelers and policy-makers as to how Hangzhou city will evolve if the corresponding factors are individually focused. This study improves our understanding of the effects of driving factors on urban expansion and future scenarios when incorporating the factors separately

Association of OX40L polymorphisms with sporadic breast cancer in northeast Chinese Han population.

OX40L is an important costimulatory molecule that plays a crucial role in the regulation of T-cell-mediated immunity. The interaction of OX40-OX40L is involved in the pathogenesis of multiple autoimmune and inflammatory diseases such as systemic lupus erythematosus (SLE), carotid artery disease and cancer. The genetic variants of OX40L can increase the risk of SLE, atherosclerosis, systemic sclerosis and show gender-specific effects in some studies. Accordingly, we performed a case-control study including 557 breast cancer patients and 580 age- and sex-matched healthy controls to investigate whether single nucleotide polymorphisms (SNPs) in the OX40L gene are associated with sporadic breast cancer susceptibility and progression in Chinese Han women. Seven SNPs of OX40L (rs6661173, rs1234313, rs3850641, rs1234315, rs12039904, rs844648 and rs10912580) were genotyped with the method of polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). The results indicated that rs3850641G allele could increase the susceptibility to breast cancer (P = 0.009662), even in the validation study (P = 0.0001515). A significant association between rs3850641 and breast cancer risk was observed under the additive model and dominant model (P = 0.01042 and 0.01942, respectively). The haplotype analysis showed that haplotype A(rs844648)A(rs10912580) was significantly associated with breast cancer, even after 10,000 permutations for haplotypes in block only (P = 0.0003). In clinicopathologic features analysis, the association between rs1234315 and C-erbB2 status was significant (P = 0.02541). Our data primarily indicates that rs3850641 of OX40L gene contributes to sporadic breast carcinogenesis in a northeast Chinese Han population

Combined therapy of CAR-IL-15/IL-15Rα-T cells and GLIPR1 knockdown in cancer cells enhanced anti-tumor effect against gastric cancer

Abstract Background Chimeric antigen receptor (CAR) T cell therapy has shown remarkable responses in hematological malignancies with several approved products, but not in solid tumors. Patients suffer from limited response and tumor relapse due to low efficacy of CAR-T cells in the complicated and immunosuppressive tumor microenvironment. This clinical challenge has called for better CAR designs and combined strategies to improve CAR-T cell therapy against tumor changes. Methods In this study, IL-15/IL-15Rα was inserted into the extracellular region of CAR targeting mesothelin. In-vitro cytotoxicity and cytokine production were detected by bioluminescence-based killing and ELISA respectively. In-vivo xenograft mice model was used to evaluate the anti-tumor effect of CAR-T cells. RNA-sequencing and online database analysis were used to identify new targets in residual gastric cancer cells after cytotoxicity assay. CAR-T cell functions were detected in vitro and in vivo after GLI Pathogenesis Related 1 (GLIPR1) knockdown in gastric cancer cells. Cell proliferation and migration of gastric cancer cells were detected by CCK-8 and scratch assay respectively after GLIPR1 were overexpressed or down-regulated. Results CAR-T cells constructed with IL-15/IL-15Rα (CAR-ss-T) showed significantly improved CAR-T cell expansion, cytokine production and cytotoxicity, and resulted in superior tumor control compared to conventional CAR-T cells in gastric cancer. GLIPR1 was up-regulated after CAR-T treatment and survival was decreased in gastric cancer patients with high GLIPR1 expression. Overexpression of GLIPR1 inhibited cytotoxicity of conventional CAR-T but not CAR-ss-T cells. CAR-T treatment combined with GLIPR1 knockdown increased anti-tumor efficacy in vitro and in vivo. Conclusions Our data demonstrated for the first time that this CAR structure design combined with GLIPR1 knockdown in gastric cancer improved CAR-T cell-mediated anti-tumor response