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Fractures are frequently encountered diseases troubling the senior population, and the research on fracture repair and the exploration of effective treatment methods are of great significance. This study aimed to clarify the effect of human umbilical cord mesenchymal stromal cell-derived extracellular vesicles (hUMSC-EVs) on the proliferation and osteogenic differentiation of autologous bone marrow stem cells (ABMSCs). The two kinds of cells were co-cultured firstly, 5-Ethynyl-2'- deoxyuridine (EDU) staining and alizarin red staining were used to detect the proliferation and osteogenic differentiation of ABMSCs. The exosomes of hUMSCs were subsequently extracted to process ABMSCs to further test the effect on the cells. The EDU positive rate of ABMSCs and Collagen II expression were elevated, whereas the TdT-mediated dUTP nick end labeling (TUNEL) positive rate and Matrix Metallopeptidase 13 (MMP13) were markedly decreased after the co-culture of hUMSCs and ABMSCs using Transwell chamber assays. The results indicated that hUMSCs could increase the proliferation of ABMSCs, reduce apoptosis, and promote matrix metabolism. The hUMSCs exosomes were separated and added to ABMSCs. As the exosomes content increased, the proliferation of ABMSCs increased simultaneously, and ABMSCs apoptosis decreased. Meanwhile, ABMSCs that migrated to the submembrane increased compared with untreated ABMSCs. Western blot, qPCR and immunofluorescence results revealed that increased exosomes contents promoted the expression of ABMSCs anabolic-related indicators gradually, while decreased the expression of catabolism-related indicators gradually. The previously described results indicated that hUMSCs promoted the proliferation and osteogenic differentiation of ABMSCs by secreting exosomes.

Artificial intelligence (AI) techniques have been widely applied to infectious disease outbreak detection and early warning, trend prediction, and public health response modeling and assessment. Such public health surveillance and response tasks of major importance pose unique technical challenges such as data sparsity, lack of positive training samples, difficulty in developing baselines and quantifying the control measures, and interwoven dependencies between spatiotemporal elements and finer-grained risk analyses through contact and social networks. Traditional public health surveillance relies heavily on statistical techniques. Recent years have seen tremendous growth of AI-enabled methods, including but not limited to deep learning–based models, complementing statistical approaches. This chapter aims to provide a systematic review of these recent advances applying AI techniques to address public health surveillance and response challenges.

There are often multiple diseases with cross immunity competing for vaccination resources. Here we investigate the optimal vaccination program in a two-layer Susceptible-Infected-Removed (SIR) model, where two diseases with cross immunity spread in the same population, and vaccines for both diseases are available. We identify three scenarios of the optimal vaccination program, which prevents the outbreaks of both diseases at the minimum cost. We analytically derive a criterion to specify the optimal program based on the costs for different vaccines., 5 pages, 3 figures

To investigate the drug release and anti-adhesion effects of a TET (tetrandrine)-loaded PDLLA (poly-dl-lactide) film. Detection of TET release in vitro was carried out by high-performance liquid chromatography (HPLC) every 2 days following immersion of the tetrandrine-loaded PDLLA film in simulated body fluid until the TET content of the eluate could not be detected. For the in vivo test, TET-loaded PDLLA films were implanted into animal laminectomy models and positive and blank control groups were also set up. Postoperative serum tests, and macroscopic and histological analyses at 1, 4, 8, and 12 weeks, were used to assess the effects of the film. Statistical analyses were performed by one-way ANOVA. The drug release of the tetrandrine-loaded PDLLA film in vitro showed two phases with a second release peak. Ultimately, the duration of continuous delivery was up to 66 days and the cumulative delivery rate was up to 93.18%. Scores for the proliferation of epidural scars or adhesion of the dura mater in the test group were much lower than those for the two control groups. Histological analysis revealed the test group had fewer inflammatory cells and fibroblasts, as well as fewer extracellular collagen fibers, and a lower histology score than those of the two control groups at all time points. Tetrandrine-loaded PDLLA film is a novel controlled drug release and anti-adhesion material in vitro and in vivo.

Network embedding aims to find a way to encode network by learning an embedding vector for each node in the network. The network often has property information which is highly informative with respect to the node's position and role in the network. Most network embedding methods fail to utilize this information during network representation learning. In this paper, we propose a novel framework, FANE, to integrate structure and property information in the network embedding process. In FANE, we design a network to unify heterogeneity of the two information sources, and define a new random walking strategy to leverage property information and make the two information compensate. FANE is conceptually simple and empirically powerful. It improves over the state-of-the-art methods on Cora dataset classification task by over 5%, more than 10% on WebKB dataset classification task. Experiments also show that the results improve more than the state-of-the-art methods as increasing training size. Moreover, qualitative visualization show that our framework is helpful in network property information exploration. In all, we present a new way for efficiently learning state-of-the-art task-independent representations in complex attributed networks. The source code and datasets of this paper can be obtained from https://github.com/GraphWorld/FANE.

The topology structure of social contacts network has a big impact on dynamic patterns of epidemic spreading and effectiveness of non-pharmaceutical interventions. Corresponding to individuals’ behavioral or functional units, people are commonly organized in small communities, meaning that most of social contacts networks tend to display community structure property. Through empirical investigation and Monte-Carlo simulation on a big H1N1 outbreak in a Chinese university campus, this paper explores the impact of community structure property of social contacts network on epidemic spreading and effectiveness of interventions. A stochastic model based on social contacts networks among students is constructed to simulate this outbreak, revealing that epidemic outbreaks commonly occur in local community. Moreover, effectiveness of three quarantine-based interventions is quantitatively studied by our proposed model, finding that community structure of social networks determines the effects these measures.

Based on the spatial units of community, village and town in Beijing, the relationship betweent HFMD morbidity and the potential risk factors has been examined. According to the 6 selected risk factors (namely population density, disposable income of urban residents, the number of medical and health institutions, the number of hospital beds, average annual temperature and average annual relative humidity) significantly related to HFMD morbidity, the prediction performance of Classical Linear Regression Model(CLRM) and Spatial Lag Model(SLM) has been compared. The results showed that SLM achieved better effect and R square reached 0.82. It was showed that spatial effect played the crucial role in the HFMD morbidity prediction and its contribution attained 88%. However, CLRM showed low prediction accuracy and bias estimation. It was demonstrated that including spatial effect item into CLRM could greatly improve the performance of HFMD morbidity prediciton model.

A new gain model for estimating speech is presented in this paper, in which there are the same parameters as MMSE-LSA gain model, but there is a different form. This new gain model prevails over the model based on maximum likelihood estimation in restraining musical noise, and is superior to MMSE-LSA gain model in both preserving weak speech components and tracking the starts and ends of speech. Further more, by introducing the future frame into averaging the original power spectrum and by modifying the undervalued noise, the algorithm based on the new gain model gets a more satisfactory performance. Experimental results demonstrate the feasibility and effectivity of the algorithm.

The efficient generation scheduling is an important task to maintain the economy and security of modern power systems. A new monthly generation scheduling model based on multiple objectives optimization is proposed to provide a feasible initial schedule for daily scheduling so that generation resources can be optimized. The proposed model is aiming at the evenness of dispatching monthly energy in spatio-temporal viewpoint. In such a condition, monthly generation schedule can provide continue operation state for each unit, and enough reserve capacity for real-time schedule to prevent accidents. Thus it can link up monthly schedule and daily schedule smoothly so as to ensure the power system against unsteady operation. The proposed multiple objectives optimization model for monthly generation scheduling proposed has four sub objectives as follows:(1) Leveling the daily loss of load probability (LOLP) in a certain month; (2) Minimizing the up/down times for all units;(3) Leveling the daily energy for each unit; (4) Leveling the daily exchange energy for each sub region. The monthly generation scheduling model will determine the optimal operation strategy for the specified month, including monthly unit commitment and daily energy of all units. The constraints of the optimization model include transmission capacity of power grid, unit generating capacity and unit maintenance schedule, and so on. The monthly generation scheduling model is decomposed into two main decision stages: monthly unit commitment problem (MUCP) and energy dispatch problem (EDP). The MUCP is a nonlinear optimization problem with high dimensionality, continuous and discrete variables, multi-objective function, as well as lots of equality and inequality constraints. In the genetic algorithm (GA) implementation, a new technique to represent candidate solutions is introduced, and a set of expert operators has been incorporated to improve the behavior of the algorithm. The proposed model has been put into application in Shandong province. Reasonable result has been gained which is presented and discussed in this paper.