The growth and development regularity and genetic parameters were described in three types of indigenous chicken in China, Shaobo, Huaixiang and Youxi Chicken, by 3 nonlinear mathematics models, Logistics, Gompertz and Bertalanfy, according to the data of Body Weight from 1 week to 10 weeks. The results showed that the growth process of the three breeds could be illustrated well by three models, and the indices of fitness were very high (more than 0.99). Among the 3 models, Gompertz model was the best for its less bias from practice. The inflexions of growth were 5.98, 5.11 and 6.16 weeks of age, and body weights were 2115.77 g, 1499.08 g and 1409.62 g, respectively in Shaobo Chicken, Huaixiang Chicken and Youxi Chicken.

The prognosis of breast cancer patients with metastases is generally poor, so it is essential to elucidate related molecules mechanisms. Forkhead Box J2 (FOXJ2) is a member of Forkhead Box transcription factors, many of which have been reported to participate in tumor migration and invasion. In this study, we showed the expression of FOXJ2 was higher in primary breast cancer tissues without lymph nodes metastases than those with, and there was statistical significance between the expression of FXOJ2 and the clinical factors. Hence, we identified a novel function of metastasis, which was not previously known for FOXJ2. Overexpression of FOXJ2 decreased the motility property of highly migrative MDA‐MB‐231 cells in vitro by wound healing assays and trans‐well migration assays, and it was concurrent with the increased expression of epithelial marker E‐cadherin and the decreased expression of mesenchymal marker vimentin by Western blot analysis, reverse transcription PCR analysis, and immunofluorescence analysis. Consistent with these observations, the repression of FOXJ2 in weakly metastatic MCF‐7 cells remarkably promoted cellular motility. Our study demonstrates that FOXJ2 can inhibit the metastasis of human breast cancer by regulating the EMT key markers E‐cadherin and vimentin. J. Cell. Biochem. 113: 2729–2737, 2012. © 2012 Wiley Periodicals, Inc.

The widespread utilization of cellulose nanofibril (CNF) has been significantly hindered by its inherent flammability. To explore the potential of using CNF aerogel as sustainable material with good fire‐retardant and thermal‐insulating properties, CNF aerogel is modified by in situ supramolecular assembly of melamine (MEL) and phytic acid (PA). This strategy addresses CNF's flammability and avoids the environment issues associated with the incorporation of traditional fire‐retardant. The modified aerogel exhibits highly porous honeycomb structure with low density and good mechanical properties. After modification with MEL–PA, the aerogel exhibits highly improved shape integrity during burning, higher thermal stability, and favorable combustion behavior for fire retardancy. The heat transfer of the modified aerogel is well hindered, which demonstrated effective thermal insulation performance. In view of the excellent thermal and fire‐retardant properties, the MEL–PA/CNF composite aerogel can be a potential fire‐retardant and thermal‐insulating material for applications such as clothing, building, and electronic devices.

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Text classification has always been an interesting issue in the research area of natural language processing (NLP). While entering the era of big data, a good text classifier is critical to achieving NLP for scientific big data analytics. With the ever-increasing size of text data, it has posed important challenges in developing effective algorithm for text classification. Given the success of deep neural network (DNN) in analyzing big data, this article proposes a novel text classifier using DNN, in an effort to improve the computational performance of addressing big text data with hybrid outliers. Specifically, through the use of denoising autoencoder (DAE) and restricted Boltzmann machine (RBM), our proposed method, named denoising deep neural network (DDNN), is able to achieve significant improvement with better performance of antinoise and feature extraction, compared to the traditional text classification algorithms. The simulations on benchmark datasets verify the effectiveness and robustness of our proposed text classifier.