Autoregressive GNN-ODE GRU Model for Network Dynamics

Revealing the continuous dynamics on the networks is essential for understanding, predicting, and even controlling complex systems, but it is hard to learn and model the continuous network dynamics because of complex and unknown governing equations, high dimensions of complex systems, and unsatisfactory observations. Moreover, in real cases, observed time-series data are usually non-uniform and sparse, which also causes serious challenges. In this paper, we propose an Autoregressive GNN-ODE GRU Model (AGOG) to learn and capture the continuous network dynamics and realize predictions of node states at an arbitrary time in a data-driven manner. The GNN module is used to model complicated and nonlinear network dynamics. The hidden state of node states is specified by the ODE system, and the augmented ODE system is utilized to map the GNN into the continuous time domain. The hidden state is updated through GRUCell by observations. As prior knowledge, the true observations at the same timestamp are combined with the hidden states for the next prediction. We use the autoregressive model to make a one-step ahead prediction based on observation history. The prediction is achieved by solving an initial-value problem for ODE. To verify the performance of our model, we visualize the learned dynamics and test them in three tasks: interpolation reconstruction, extrapolation prediction, and regular sequences prediction. The results demonstrate that our model can capture the continuous dynamic process of complex systems accurately and make precise predictions of node states with minimal error. Our model can consistently outperform other baselines or achieve comparable performance

Go viral or go broadcast? Characterizing the virality and growth of cascades

Quantifying the virality of cascades is an important question across disciplines such as the transmission of disease, the spread of information and the diffusion of innovations. An appropriate virality metric should be able to disambiguate between a shallow, broadcast-like diffusion process and a deep, multi-generational branching process. Although several valuable works have been dedicated to this field, most of them fail to take the position of the diffusion source into consideration, which makes them fall into the trap of graph isomorphism and would result in imprecise estimation of cascade virality inevitably under certain circumstances. In this paper, we propose a root-aware approach to quantifying the virality of cascades with proper consideration of the root node in a diffusion tree. With applications on synthetic and empirical cascades, we show the properties and potential utility of the proposed virality measure. Based on preferential attachment mechanisms, we further introduce a model to mimic the growth of cascades. The proposed model enables the interpolation between broadcast and viral spreading during the growth of cascades. Through numerical simulations, we demonstrate the effectiveness of the proposed model in characterizing the virality of growing cascades. Our work contributes to the understanding of cascade virality and growth, and could offer practical implications in a range of policy domains including viral marketing, infectious disease and information diffusion.Comment: 10 pages, 15 figures, 1 tabl

Preparation and pre-clinical characterization of sustainedrelease ketoprofen implants for the management of pain and inflammation in osteoarthritis

Purpose: To prepare and evaluate sustained-release ketoprofen implants for prolonged drug release and activity.Methods: Ketoprofen implants were prepared with poly (lactic-co-glycolic acid) (PLGA) and chitosan in the form of tablets. The implants were analyzed for drug loading, thickness, hardness, swelling, in vitro drug release, as well as in vivo analgesic and anti-inflammatory activities.Results: The implants were round, smooth in appearance, uniform in thickness and showed no cracks or physical defects on the surface. Their friability was < 1 % while drug content ranged from 89.98 ± 2.06 to 92.95 ± 1.65 %. In vitro drug release ranged from 70.23 to 92.04 % at the end of 5 days. Implants containing higher amounts of PLGA produced the highest swelling (40.24 ± 1.08 %). Implant IKT3 showed maximum analgesic activity (7.75 ± 1.00 s) and shortest time of maximum analgesia (2.5 h) in hot plate method. Inhibition of rat paw edema for IKT1, IKT2 and IKT3 was 79.95, 69.98 and 82.24 %, respectively, after 24 h.Conclusion: Ketoprofen-loaded implant IKT3 (4:4:2 ratio of PLGA, chitosan and ketoprofen) provides relatively quick onset and prolonged duration of analgesic effect. Thus, ketoprofen implants have a potential for development into therapeutic products for prolonged management of pain and inflammation in osteoarthritis.Keywords: Osteoarthritis, Ketoprofen implant, Prolonged analgesia, Poly(lactic-co-glycolic acid), Chitosa

Phylogenetic detection of numerous gene duplications shared by animals, fungi and plants

BACKGROUND: Gene duplication is considered a major driving force for evolution of genetic novelty, thereby facilitating functional divergence and organismal diversity, including the process of speciation. Animals, fungi and plants are major eukaryotic kingdoms and the divergences between them are some of the most significant evolutionary events. Although gene duplications in each lineage have been studied extensively in various contexts, the extent of gene duplication prior to the split of plants and animals/fungi is not clear. RESULTS: Here, we have studied gene duplications in early eukaryotes by phylogenetic relative dating. We have reconstructed gene families (with one or more orthogroups) with members from both animals/fungi and plants by using two different clustering strategies. Extensive phylogenetic analyses of the gene families show that, among nearly 2,600 orthogroups identified, at least 300 of them still retain duplication that occurred before the divergence of the three kingdoms. We further found evidence that such duplications were also detected in some highly divergent protists, suggesting that these duplication events occurred in the ancestors of most major extant eukaryotic groups. CONCLUSIONS: Our phylogenetic analyses show that numerous gene duplications happened at the early stage of eukaryotic evolution, probably before the separation of known major eukaryotic lineages. We discuss the implication of our results in the contexts of different models of eukaryotic phylogeny. One possible explanation for the large number of gene duplication events is one or more large-scale duplications, possibly whole genome or segmental duplication(s), which provides a genomic basis for the successful radiation of early eukaryotes