Modeling the Process of Fabricating Cell-Encapsulated Tissue Scaffolds and the Process-Induced Cell Damage

Tissue engineering is an emerging field aimed to combine biological, engineering and material methods to create a biomimetic three dimensional (3D) environment to control cells proliferation and functional tissue formation. In such an artificial structural environment, a scaffold, made from biomaterial(s), plays an essential role by providing a mechanical support and biological guidance platform. Hence, fabrication of tissue scaffolds is of a fundamental importance, yet a challenging task, in tissue engineering. This task becomes more challenging if living cells need to be encapsulated in the scaffolds so as to fabricate scaffolds with structures to mimic the native ones, mainly due to the issue of process-induced cell damage. This research aims to develop novel methods to model the process of fabricating cell-encapsulated scaffolds and process-induced cell damage. Particularly, this research focuses on the scaffold fabrication process based on the dispensing-based rapid prototyping technique - one of the most promising scaffold fabrication methods nowadays, by which a 3D scaffold is fabricated by laying down multiple, precisely formed layers in succession. In the dispensing-based scaffold fabrication process, the flow behavior of biomaterials solution can significantly affect the flow rate of material dispensed, thus the structure of scaffold fabricated. In this research, characterization of flow behavior of materials was studied; and models to represent the flow behaviour and its influence on the scaffold structure were developed. The resultant models were shown able to greatly improve the scaffold fabrication in terms of process parameter determination. If cells are encapsulated in hydrogel for scaffold fabrication, cell density can affect the mechanical properties of hydrogel scaffolds formed. In this research, the influence of cell density on mechanical properties of hydrogel scaffolds was investigated. Furthermore, finite element analysis (FEA) of mechanical properties of scaffolds with varying cell densities was performed.The results show that the local stress and strain energy on cells varies at different cell densities. The method developed may greatly facilitate hydrogel scaffolds design to minimize cell damage in scaffold and promote tissue regeneration. . In the cell-encapsulated scaffold fabrication process, cells inevitably suffer from mechanical forces and other process-induced hazards. In such a harsh environment, cells deform and may be injured, even damaged due to mechanical breakage of cell membrane. In this research, three primary physical variables: shear stress, exposure time, and temperature were examined and investigated with regard to their effects on cell damage. Cell damage laws through the development phenomenal models and computational fluidic dynamic (CFD) models were established; and their applications to the cell-encapsulated scaffold fabrication process were pursued. The results obtained show these models and modeling methods not only allow one to optimize process parameters to preserve cell viability but also provide a novel strategy to probe cell damage mechanism in microscopic view

Intermediate-pressure phases of cerium studied by an LDA + Gutzwiller method

The thermodynamic stable phase of cerium metal in the intermediate pressure regime (5.0--13.0 GPa) is studied in detail by the newly developed local-density approximation (LDA)+ Gutzwiller method, which can include the strong correlation effect among the 4\textit{f} electrons in cerium metal properly. Our numerical results show that the $\alpha"$ phase, which has the distorted body-centered-tetragonal structure, is the thermodynamic stable phase in the intermediate pressure regime and all the other phases including the $\alpha'$ phase ($\alpha$-U structure), $\alpha$ phase (fcc structure), and bct phases are either metastable or unstable. Our results are quite consistent with the most recent experimental data.Comment: 17 pages, 7 figure

Adversarial Training Towards Robust Multimedia Recommender System

With the prevalence of multimedia content on the Web, developing recommender solutions that can effectively leverage the rich signal in multimedia data is in urgent need. Owing to the success of deep neural networks in representation learning, recent advance on multimedia recommendation has largely focused on exploring deep learning methods to improve the recommendation accuracy. To date, however, there has been little effort to investigate the robustness of multimedia representation and its impact on the performance of multimedia recommendation. In this paper, we shed light on the robustness of multimedia recommender system. Using the state-of-the-art recommendation framework and deep image features, we demonstrate that the overall system is not robust, such that a small (but purposeful) perturbation on the input image will severely decrease the recommendation accuracy. This implies the possible weakness of multimedia recommender system in predicting user preference, and more importantly, the potential of improvement by enhancing its robustness. To this end, we propose a novel solution named Adversarial Multimedia Recommendation (AMR), which can lead to a more robust multimedia recommender model by using adversarial learning. The idea is to train the model to defend an adversary, which adds perturbations to the target image with the purpose of decreasing the model's accuracy. We conduct experiments on two representative multimedia recommendation tasks, namely, image recommendation and visually-aware product recommendation. Extensive results verify the positive effect of adversarial learning and demonstrate the effectiveness of our AMR method. Source codes are available in https://github.com/duxy-me/AMR.Comment: TKD

Some dynamical properties of delayed weakly reversible mass-action systems

This paper focuses on the dynamical properties of delayed complex balanced systems. We first study the relationship between the stoichiometric compatibility classes of delayed and non-delayed systems. Using this relation we give another way to derive the existence of positive equilibrium in each stoichiometric compatibility class for delayed complex balanced systems. And if time delays are constant, the result can be generalized to weakly reversible networks. Also, by utilizing the Lyapunov-Krasovskii functional, we can obtain a long-time dynamical property about $\omega$-limit set of the complex balanced system with constant time delays. An example is also provided to support our results

Predicting changes in protein thermostability brought about by single- or multi-site mutations

<p>Abstract</p> <p>Background</p> <p>An important aspect of protein design is the ability to predict changes in protein thermostability arising from single- or multi-site mutations. Protein thermostability is reflected in the change in free energy (ΔΔ<it>G</it>) of thermal denaturation.</p> <p>Results</p> <p>We have developed predictive software, Prethermut, based on machine learning methods, to predict the effect of single- or multi-site mutations on protein thermostability. The input vector of Prethermut is based on known structural changes and empirical measurements of changes in potential energy due to protein mutations. Using a 10-fold cross validation test on the M-dataset, consisting of 3366 mutants proteins from ProTherm, the classification accuracy of random forests and the regression accuracy of random forest regression were slightly better than support vector machines and support vector regression, whereas the overall accuracy of classification and the Pearson correlation coefficient of regression were 79.2% and 0.72, respectively. Prethermut performs better on proteins containing multi-site mutations than those with single mutations.</p> <p>Conclusions</p> <p>The performance of Prethermut indicates that it is a useful tool for predicting changes in protein thermostability brought about by single- or multi-site mutations and will be valuable in the rational design of proteins.</p

Influence of Acetaldehyde Induction on Monomeric and Polymeric Polyphenols in Wine using the Polyphenol/Protein-binding Model

Polyphenols make a substantial contribution to the sensory properties of wine, and their evolution is affected by the acetaldehyde present during fermentation and ageing. In this work, five typical monomeric phenolic standards and three different polymeric flavanol fractions separated from wine were tested for polyphenol/protein binding by means of circular dichroism measurement and fluorescence spectrum assay in the presence or absence of acetaldehyde, and the formation of new oligomeric compounds linked by ethyl bridges was observed through HPLC-MS analyses. The results show that the protein-binding ability of these monomers was in the order of gallic acid &gt; caffeic acid &gt; quercetin &gt; (+)-catechin &gt; (-)-epicatechin, while acetaldehyde exerted a stronger effect on (+)-catechin and (-)-epicatechin monomers. Moreover, different wine fractions had different responses when reacted with proteins with the participation of acetaldehyde, while the polymeric proanthocyanidins produced the largest value (84.67%) of the salivary protein precipitation index and the strongest fluorescence-quenching effect

Outer Product-based Neural Collaborative Filtering

In this work, we contribute a new multi-layer neural network architecture named ONCF to perform collaborative filtering. The idea is to use an outer product to explicitly model the pairwise correlations between the dimensions of the embedding space. In contrast to existing neural recommender models that combine user embedding and item embedding via a simple concatenation or element-wise product, our proposal of using outer product above the embedding layer results in a two-dimensional interaction map that is more expressive and semantically plausible. Above the interaction map obtained by outer product, we propose to employ a convolutional neural network to learn high-order correlations among embedding dimensions. Extensive experiments on two public implicit feedback data demonstrate the effectiveness of our proposed ONCF framework, in particular, the positive effect of using outer product to model the correlations between embedding dimensions in the low level of multi-layer neural recommender model. The experiment codes are available at: https://github.com/duxy-me/ConvNCFComment: IJCAI 201