Temperature-responsive culture surfaces for insect cell sheets to fabricate a bioactuator

ABSTRACT

For the first time, we have fabricated insect-derived cell sheets by using temperature-responsive culture surfaces having a phase-transition temperature below 25°C. We prepared the temperature-responsive cell culture surfaces (tissue culture polystyrene, TCPS) by grafting a copolymeric gel consisting of hydrophobic N-tert-butylacrylamide (tBAAm) and N-isopropylacrylamide (IPAAm) units. First, to characterize the hydrophilic and hydrophobic properties of the copolymeric gel-grafted surfaces, static water contact angles of each surface were measured at various temperatures. By increasing the amount of tBAAm in the grafted copolymeric gel, the transition temperature of the gel was shifted to lower temperatures. At 25°C, the grafted copolymeric gel was dehydrated, and the insect-derived cells (AeAl2 cells) adhered on all the copolymeric gel-grafted surfaces. At 20°C, AeAl2 cells cannot adhere on the P(IPAAm-1.62tBAAm)-TCPS surface (the initial molar ratio of IPAAm and tBAAm (tBAAm?=?1.62 mol%)) better than on other surfaces (TCPS and tBAAm?=?4.88, 8.17 mol%). These two findings implied that the lower critical solution temperature of the copolymeric gel-grafted-TCPS existed from 20°C to 25°C. The laminin-coated P(IPAAm-1.62tBAAm)-TCPS surface showed temperature-dependent cell attachment and detachment properties, while AeAl2 cells were not detached from the extracellular matrix uncoated P(IPAAm-1.62tBAAm)-TCPS surface. AeAl2 cells and insect muscle cells were harvested as the respective sheets.     

Genetic algorithm and self organizing map based fuzzy hybrid intelligent method for color image segmentation

The grouping of pixels based on some similarity criteria is called image segmentation. In this paper the problem of color image segmentation is considered as a clustering problem and a fixed length genetic algorithm (GA) is used to handle it. The effectiveness of GA depends on the objective function (fitness function) and the initialization of the population. A new objective function is proposed to evaluate the quality of the segmentation and the fitness of a chromosome. In fixed length genetic algorithm the chromosomes have same length, which is normally set by the user. Here, a self organizing map (SOM) is used to determine the number of segments in order to set the length of a chromosome automatically. An opposition based strategy is adopted for the initialization of the population in order to diversify the search process. In some cases the proposed method makes the small regions of an image as separate segments, which leads to noisy segmentation. A simple ad hoc mechanism is devised to refine the noisy segmentation. The qualitative and quantitative results show that the proposed method performs better than the state-of-the-art methods.     

Domain structure-based transfer learning for cross-domain word representation

Cross-domain word representation aims to learn high-quality semantic representations in an under-resourced domain by leveraging information in a resourceful domain. However, most existing methods mainly transfer the semantics of common words across domains, ignoring the semantic relations among domain-specific words. In this paper, we propose a domain structure-based transfer learning method to learn cross-domain representations by leveraging the relations among domain-specific words. To accomplish this, we first construct a semantic graph to capture the latent domain structure using domain-specific co-occurrence information. Then, in the domain adaptation process, beyond domain alignment, we employ Laplacian Eigenmaps to ensure the domain structure is consistently distributed in the learned embedding space. As such, the learned cross-domain word representations not only capture shared semantics across domains, but also maintain the latent domain structure. We performed extensive experiments on two tasks, namely sentiment analysis and query expansion. The experiment results show the effectiveness of our method for tasks in under-resourced domains.     

融入局部几何特征的流形谱聚类图像分割

为了改善谱聚类图像分割的精准性和时效性,文中提出融入局部几何特征的流形谱聚类图像分割算法.首先,考虑图像数据的流形结构,在数据点的K近邻域内执行局部PCA,得到数据间本征维数的关系.然后,引入流形学习中的局部线性重构技术,通过混合线性分析器得到数据间局部切空间的相似性,结合二者构造含有局部几何特征的相似性矩阵.再利用Nystr m技术逼近待分割图像的特征向量,对构造的k个主特征向量执行谱聚类.最后,在Berkeley数据集上的对比实验验证文中算法的准确性和时效性优势.     

Core dimensions of food-related lifestyle: A new instrument for measuring food involvement,innovativeness and responsibility

Segmentation is crucial for targeting product development initiatives and marketing communication nationally as well as internationally. In this paper we use the Food Related Lifestyle instrument that has been applied in the food arena for many years as a ‘stepping stone’ to develop a contemporary, targeted and smaller version of the Food Related Lifestyle instrument, still following the original theoretical framework. In particular we focus on three dimensions that have proven to be of core value in segmentation: food involvement; food innovativeness; and, food responsibility. Based on data collection in six countries (DK, AU, HU, UK, USA and NZ) across two rounds from 2017 to 2019 (total N = 3396), we propose a new core instrument consisting of 15 items that have been tested for cross-cultural validity. Next, we used these three dimensions for segmentation across the six countries by applying multi-level latent class analysis. A solution leading to five different segments could be identified; the foodies, the moderates, the adventurous, the uninvolved and the conservatives. The segments were profiled by means of Schwartz’s ten value domains and measures of self-reported food-related behaviour to check for nomonological validity. We conclude that the 15 items were cross-culturally valid, could be used for segmentation across six countries, and that segment profiling by means of Schwartz values and behavioural items were in line with the theoretical background.     

Customers segmentation in eco-friendly hotels using multi-criteria and machine learning techniques

This study aims to investigate the travellers' choice behaviour towards green hotels through existing online travel reviews on TripAdvisor. Accordingly, a method combining segmentation and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) techniques was developed to segment travellers based on their provided reviews and to prioritize green hotel attributes based on their level of importance in each segment. The data were taken from travellers' online reviews of Malaysian eco-friendly hotels on TripAdvisor. The results showed that the sleep quality was one of the most imporant factors for eco-hotel selection in the majority of segments. The developed method in this study was able to analyse travellers’ reviews and ratings on eco-friendly hotels to identify the future choice behaviour and aid travellers in their decision-making process. The study provides new insights for hotel managers and green policy makers on developing environmental-friendly practices.     

Unsupervised regions based segmentation using object discovery

We present a new unsupervised algorithm to discovery and segment out common objects from multiple images. Compared with previous cosegmentation methods, our algorithm performs well even when the appearance variations in the foregrounds are more substantial than those in some areas of the backgrounds. Our algorithm mainly includes two parts: the foreground object discovery scheme and the iterative region allocation algorithm. Two terms, a region-saliency prior and a region-repeatness measure, are introduced in the foreground object discovery scheme to detect the foregrounds without any supervisory information. The iterative region allocation algorithm searches the optimal solution for the final segmentation with the constraints from a maximal spanning tree, and an effective color-based model is utilized during this process. The comparative experimental results show that the proposed algorithm matches or outperforms several previous methods on several standard datasets.     

Economic Optimization of Distribution Leakage Control

E vry WATER DISTRIBUTION system has an economic optimum level of leakage. To maintain that optimum level of leakage there is a corresponding optimum level of leakage control activity.
Despite much work on this subject, particularly in recent years with publication of DoE Report No. 26¹, many undertakers remain unclear about their economic optimum leakage level and how much effort is required to maintain that level.
This paper describes development of an economic model which attempts to quantify optimum leakage through measurement of parameters which should be readily available or obtainable.
The model was originally developed specifically for routine sounding. However, extension of the basic principles allows development of a general optimization theory. The paper then shows how the theory can be applied to optimization of district metering     

生态研究文献关键词分析对风景园林学科的启示

关键词分析是文献计量的一种方法。它能快速、客观和直观地了解某个学术领域的研究热点和趋势。随着人类对自然环境的重视,近年来有关生态的研究逐渐从纯粹的郊野自然走向城市自然。搜集了2004-2013年10年间《景观与城市规划》(Landscape and Urban Planning)期刊刊载的有关风景园林学生态方向的文献,将文献中的关键词按研究对象、研究方法以及研究热点进行统计和分类,提取出高频关键词,并根据文献摘要内容,对有关风景园林学科的生态研究进展进行了综述,总结出近10年期刊中有关风景园林学科生态研究热点,为掌握风景园林学科的发展动态提供依据。