Multifunctional fibrous scaffolds, which combine the capabilities of biomimicry to the native tissue architecture and shape memory effect (SME), are highly promising for the realization of functional tissue-engineered products with minimally invasive surgical implantation possibility. In this study, fibrous scaffolds of biodegradable poly(d,l-lactide-co-trimethylene carbonate) (denoted as PDLLA-co-TMC, or PLMC) with shape memory properties were fabricated by electrospinning. Morphology, thermal and mechanical properties as well as SME of the resultant fibrous structure were characterized using different techniques. And rat calvarial osteoblasts were cultured on the fibrous PLMC scaffolds to assess their suitability for bone tissue engineering. It is found that by varying the monomer ratio of DLLA:TMC from 5:5 to 9:1, fineness of the resultant PLMC fibers was attenuated from ca. 1500 down to 680 nm. This also allowed for readily modulating the glass transition temperature Tg (i.e., the switching temperature for actuating shape recovery) of the fibrous PLMC to fall between 19.2 and 44.2 °C, a temperature range relevant for biomedical applications in the human body. The PLMC fibers exhibited excellent shape memory properties with shape recovery ratios of Rr > 94% and shape fixity ratios of Rf > 98%, and macroscopically demonstrated a fast shape recovery (∼10 s at 39 °C) in the pre-deformed configurations. Biological assay results corroborated that the fibrous PLMC scaffolds were cytocompatible by supporting osteoblast adhesion and proliferation, and functionally promoted biomineralization-relevant alkaline phosphatase expression and mineral deposition. We envision the wide applicability of using the SME-capable biomimetic scaffolds for achieving enhanced efficacy in repairing various bone defects (e.g., as implants for healing bone screw holes or as barrier membranes for guided bone regeneration).
In this paper, an influence model is used to recognize functional roles played during meetings. Previous works on the same corpus demonstrated a high recognition accuracy using SVMs with RBF kernels. In this paper, we discuss the problems of that approach, mainly over-fitting, the curse of dimensionality and the inability to generalize to different group configurations. We present results obtained with an influence modeling method that avoid these problems and ensures both greater robustness and generalization capability.
The co-evolution of social relationships and individual behavior in time and space has important implications, but is poorly understood because of the difficulty closely tracking the everyday life of a complete community. We offer evidence that relationships and behavior co-evolve in a student dormitory, based on monthly surveys and location tracking through resident cellular phones over a period of nine months. We demonstrate that a Markov jump process could capture the co-evolution in terms of the rates at which residents visit places and friends.
Deep latent variable models (LVM) such as variational auto-encoder (VAE) have recently played an important role in text generation.One key factor is the exploitation of smooth latent structures to guide the generation. However, the representation power of VAEs is limited due to two reasons: (1) the Gaussian assumption is often made on the variational posteriors; and meanwhile (2) a notorious "posterior collapse" issue occurs. In this paper, we advocate sample-based representations of variational distributions for natural language, leading to implicit latent features, which can provide flexible representation power compared with Gaussian-based posteriors. We further develop an LVM to directly match the aggregated posterior to the prior. It can be viewed as a natural extension of VAEs with a regularization of maximizing mutual information, mitigating the "posterior collapse" issue. We demonstrate the effectiveness and versatility of our models in various text generation scenarios, including language modeling, unaligned style transfer, and dialog response generation. The source code to reproduce our experimental results is available on GitHub 1 .
Brown japonica rice was treated with 60 Co γ irradiation at doses of 0, 0.2, 0.5, 1.0, and 2.0 kGy immediately after harvesting. The effects of irradiation on physicochemical, structural, and sensory properties during long-term storage (18 months) were investigated. The study revealed that the pasting properties, including peak, through, breakdown, final, and setback viscosities, decrease considerably in a dose-dependent manner and vary differently during 18 months of storage. Irradiation reduced the free fatty acid (FFA) content in comparison with unirradiated brown rice with long-term storage (from 12 to 18 months). Scanning electron microscope (SEM) observation showed that the mean range and shape of starch granules did not vary significantly. However, dark spots developed among starch granules and the narrow cracks became wider with increasing irradiation dose and storage time. During sensory evaluation, extremely low scores for odor and overall acceptability were obtained for medium-dose irradiated rice (1.0 and 2.0 kGy); however, no significant difference was found in acceptability between low-dose irradiated rice (0.2 and 0.5 kGy) and the control rice (0 kGy). Overall, low-dose (0.5 kGy or below) irradiation seems to be a promising alternative treatment to increase brown rice shelf life, without affecting the physicochemical and structural characteristics and sensory acceptability.
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