From Micro to Macro: Uncovering and Predicting Information Cascading Process with Behavioral Dynamics

Cascades are ubiquitous in various network environments. How to predict these cascades is highly nontrivial in several vital applications, such as viral marketing, epidemic prevention and traffic management. Most previous works mainly focus on predicting the final cascade sizes. As cascades are typical dynamic processes, it is always interesting and important to predict the cascade size at any time, or predict the time when a cascade will reach a certain size (e.g. an threshold for outbreak). In this paper, we unify all these tasks into a fundamental problem: cascading process prediction. That is, given the early stage of a cascade, how to predict its cumulative cascade size of any later time? For such a challenging problem, how to understand the micro mechanism that drives and generates the macro phenomenons (i.e. cascading proceese) is essential. Here we introduce behavioral dynamics as the micro mechanism to describe the dynamic process of a node's neighbors get infected by a cascade after this node get infected (i.e. one-hop subcascades). Through data-driven analysis, we find out the common principles and patterns lying in behavioral dynamics and propose a novel Networked Weibull Regression model for behavioral dynamics modeling. After that we propose a novel method for predicting cascading processes by effectively aggregating behavioral dynamics, and propose a scalable solution to approximate the cascading process with a theoretical guarantee. We extensively evaluate the proposed method on a large scale social network dataset. The results demonstrate that the proposed method can significantly outperform other state-of-the-art baselines in multiple tasks including cascade size prediction, outbreak time prediction and cascading process prediction.Comment: 10 pages, 11 figure

Dual-Functional PLGA Nanoparticles Co-Loaded with Indocyanine Green and Resiquimod for Prostate Cancer Treatment

Purpose: With the advance of screening techniques, there is a growing number of low-risk or intermediate-risk prostate cancer (PCa) cases, remaining a serious threat to men's health. To obtain better efficacy, a growing interest has been attracted to develop such emerging treatments as immunotherapy and focal therapy. However, few studies offer guidance on whether and how to combine these modalities against PCa. This study was designed to develop dual-functional nanoparticles (NPs) which combined photothermal therapy (PTT) with immunotherapy and determine the anti-tumor efficacy for PCa treatment. Methods: By a double emulsion technique, the drug nanocarrier, poly(lactic-co-glycolic acid) or PLGA, was applied for co-loading of a fluorescent dye, indocyanine green (ICG) and a toll-like receptor 7/8 (TLR7/8) agonist resiquimod (R848) to synthesize PLGA-ICG-R848 NPs. Next, we determined their characteristic features and evaluated whether they inhibited the cell viability in multiple PCa cell lines. After treatment with PLGA-ICG-R848, the maturation markers of bone marrow-derived dendritic cells (BMDCs) were detected by flow cytometry. By establishing a subcutaneous xenograft model of mouse PCa, we explored both the anti-tumor effect and immune response following the NPs-based laser ablation. Results: With a mean diameter of 157.7 nm, PLGA-ICG-R848 exhibited no cytotoxic effect in PCa cells, but they significantly decreased RM9 cell viability to (3.9 +/- 1.0)% after laser irradiation. Moreover, PLGA-ICG-R848 promoted BMDCs maturation with the significantly elevated proportions of CD11c+CD86+ and CD11c+CD80+ cells. Following PLGA-ICG-R848-based laser ablation in vivo, the decreased bioluminescent signals indicated a significant inhibition of PCa growth, while the ratio of splenic natural killer (NK) cells in PLGA-ICG-R848 was (3.96 +/- 1.88)% compared with (0.99 +/- 0.10)% in PBS group, revealing the enhanced immune response against PCa. Conclusion: The dual-functional PLGA-ICG-R848 NPs under laser irradiation exhibit the anti-tumor efficacy for PCa treatment by combining PTT with immunotherapy

Dual-Functional PLGA Nanoparticles Co-Loaded with Indocyanine Green and Resiquimod for Prostate Cancer Treatment

Purpose: With the advance of screening techniques, there is a growing number of low-risk or intermediate-risk prostate cancer (PCa) cases, remaining a serious threat to men's health. To obtain better efficacy, a growing interest has been attracted to develop such emerging treatments as immunotherapy and focal therapy. However, few studies offer guidance on whether and how to combine these modalities against PCa. This study was designed to develop dual-functional nanoparticles (NPs) which combined photothermal therapy (PTT) with immunotherapy and determine the anti-tumor efficacy for PCa treatment. Methods: By a double emulsion technique, the drug nanocarrier, poly(lactic-co-glycolic acid) or PLGA, was applied for co-loading of a fluorescent dye, indocyanine green (ICG) and a toll-like receptor 7/8 (TLR7/8) agonist resiquimod (R848) to synthesize PLGA-ICG-R848 NPs. Next, we determined their characteristic features and evaluated whether they inhibited the cell viability in multiple PCa cell lines. After treatment with PLGA-ICG-R848, the maturation markers of bone marrow-derived dendritic cells (BMDCs) were detected by flow cytometry. By establishing a subcutaneous xenograft model of mouse PCa, we explored both the anti-tumor effect and immune response following the NPs-based laser ablation. Results: With a mean diameter of 157.7 nm, PLGA-ICG-R848 exhibited no cytotoxic effect in PCa cells, but they significantly decreased RM9 cell viability to (3.9 +/- 1.0)% after laser irradiation. Moreover, PLGA-ICG-R848 promoted BMDCs maturation with the significantly elevated proportions of CD11c+CD86+ and CD11c+CD80+ cells. Following PLGA-ICG-R848-based laser ablation in vivo, the decreased bioluminescent signals indicated a significant inhibition of PCa growth, while the ratio of splenic natural killer (NK) cells in PLGA-ICG-R848 was (3.96 +/- 1.88)% compared with (0.99 +/- 0.10)% in PBS group, revealing the enhanced immune response against PCa. Conclusion: The dual-functional PLGA-ICG-R848 NPs under laser irradiation exhibit the anti-tumor efficacy for PCa treatment by combining PTT with immunotherapy

Th17/Treg Cells Imbalance and GITRL Profile in Patients with Hashimoto’s Thyroiditis

published_or_final_versio

Repurposing of posaconazole as a hedgehog/SMO signaling inhibitor for embryonal rhabdomyosarcoma therapy

Posaconazole (POS) is a novel antifungal agent, which has been repurposed as an anti-tumor drug for its potential inhibition of Hedgehog signaling pathway. Hedgehog pathway is reported to be abnormally activated in embryonal rhabdomyosarcoma (ERMS), this study aimed to reveal whether POS could inhibit Hedgehog signaling pathway in ERMS. Following POS treatment, XTT viability assay was used to determine the cell proliferation of ERMS cell lines. Protein changes related to Hedgehog signaling, cell cycle and autophagy were detected by Western blot. The cell cycle distribution was analyzed by flow cytometry. Moreover, a subcutaneous tumor mouse model of ERMS was established to assess the anti-tumor effect of POS. POS was found to inhibit tumor progression by inducing G0/G1 arrest and autophagy of RD, RMS-YM, and KYM-1 cells dose-dependently. Western blot demonstrated that POS downregulated the expressions of SMO, Gli1, c-Myc, CDK4, and CDK6, while upregulated the expressions of autophagy-related proteins. Immunofluorescence microscopy revealed a significant increase of LC3B puncta in POS-treated ERMS cells. Furthermore, POS treatment led to a significant inhibition of tumor growth in mice bearing ERMS. Our findings could provide a theoretical basis and have important clinical implications in developing POS as a promising agent against ERMS by targeting Hedgehog pathway

Numerical Analysis of Stress Gradient and Traps Effects on Carbon Diffusion in AISI 316L during Low Temperature Gas Phase Carburization

In order to elucidate the roles of the composition-induced stress gradient and the traps formed by chromium atoms in carbon diffusion in AISI 316L austenitic stainless steel during low temperature gas phase carburization, the carbon concentration-depth profiles were analyzed by a diffusion model considering the composition-induced stress gradient and the trapping effect. The results show that the carbon concentration-depth profiles calculated by this model show good agreement with the experimental results. The composition-induced compressive stress gradient can enhance the carbon diffusion but reduce the surface carbon concentration; these effects are not pronounced. Carbon atoms prefer to occupy the trap sites, and the detrapping activation energy (Qt = 33 kJ·mol−1) was deduced from fitting the experimental carbon concentration-depth profile. Furthermore, this applied diffusion model can be used to interpret the enhanced carbon diffusion in low temperature carburized AISI 316L

Voice disorder classification using convolutional neural network based on deep transfer learning

Abstract Voice disorders are very common in the global population. Many researchers have conducted research on the identification and classification of voice disorders based on machine learning. As a data-driven algorithm, machine learning requires a large number of samples for training. However, due to the sensitivity and particularity of medical data, it is difficult to obtain sufficient samples for model learning. To address this challenge, this paper proposes a pretrained OpenL3-SVM transfer learning framework for the automatic recognition of multi-class voice disorders. The framework combines a pre-trained convolutional neural network, OpenL3, and a support vector machine (SVM) classifier. The Mel spectrum of the given voice signal is first extracted and then input into the OpenL3 network to obtain high-level feature embedding. Considering the effects of redundant and negative high-dimensional features, model overfitting easily occurs. Therefore, linear local tangent space alignment (LLTSA) is used for feature dimension reduction. Finally, the obtained dimensionality reduction features are used to train the SVM for voice disorder classification. Fivefold cross-validation is used to verify the classification performance of the OpenL3-SVM. The experimental results show that OpenL3-SVM can effectively classify voice disorders automatically, and its performance exceeds that of the existing methods. With continuous improvements in research, it is expected to be considered as auxiliary diagnostic tool for physicians in the future

Uncovering and predicting the dynamic process of information cascades with survival model

Cascades are ubiquitous in various network environments. Predicting these cascades is decidedly nontrivial in various important applications, such as viral marketing, epidemic prevention, and traffic management. Most previous works have focused on predicting the final cascade sizes. As cascades are dynamic processes, it is always interesting and important to predict the cascade size at any given time, or to predict the time when a cascade will reach a certain size (e.g., the threshold for an outbreak). In this paper, we unify all these tasks into a fundamental problem: cascading process prediction. That is, given the early stage of a cascade, can we predict its cumulative cascade size at any later time? For such a challenging problem, an understanding of the micromechanism that drives and generates the macrophenomena (i.e., the cascading process) is essential. Here, we introduce behavioral dynamics as the micromechanism to describe the dynamic process of an infected node’s neighbors getting infected by a cascade (i.e., one-hop sub-cascades). Through data-driven analysis, we find out the common principles and patterns lying in the behavioral dynamics and propose the novel NEtworked WEibull Regression model for modeling it. We also propose a novel method for predicting cascading processes by effectively aggregating behavioral dynamics and present a scalable solution to approximate the cascading process with a theoretical guarantee. We evaluate the proposed method extensively on a large-scale social network dataset. The results demonstrate that the proposed method can significantly outperform other state-of-the-art methods in multiple tasks including cascade size prediction, outbreak time prediction, and cascading process prediction