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Copyright of Journal of Laser Biology / Acta Laser Biology Sinica is the property of Acta Laser Biology Sinica Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science Technology & Engineering is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A laboratory investigation of solidilication ol copper contaminated soil by Ily ash based geopolymer with bottom ash(BA-FAG) was carried out The ellects of curing time, binder content and initial copper content on the solidilication ol copper contaminated soil were studied by Unconlined Compressive Strength (UCS), toxic leaching test, X-Ray Dillraction(XRD), scanning Electron Microscopy(SEM) and X-ray Photoelectron Spectroscopy (XPS). The results showed that the UCS value of BA-FAG solidified soil can be increased together with the decrease of leaching concentration ol copper ion by increasing binder content and curing time. The initial concentration of copper ion in contaminated soil negatively correlated with the strength ol solidified soil, and positively correlated with leaching concentration. The copper contaminated soil with initial concentration of no more than 6 000 mg/kg and 10 000 mg/kg can be solidified by 2()% and 30 % binder contents, respectively. Meanwhile, the UCS values of solidified soil at 28 days of curing are no less than 0. 3 58 MPa and 0. 4 57 MPa, respectively, and the leaching concentration is lower than 100 mg/L at the conditions of both acidic and neutral leaching. The copper in the contaminated soil mainly exists in the form of Cu2+ and Cu(OH) 2 precipitation during the physical encapsulation by BA-FAG. [ABSTRACT FROM AUTHOR]

探讨了中药地龙水提液和醇提液有无促进成骨细胞增殖、分化和基质矿化的作用及对大鼠牙槽骨吸收疗效进行了研究.制备地龙的水相及醇相提取液，以小鼠成骨细胞株MC3T3-E1作为体外药效的试验模型，分别通过MTT法.流式细胞术，碱性磷酸酶活性测定，骨钙素检测和Von kossa基质矿化染色法观察上述药物的促细胞增殖、分化、基质矿化作用.以实验牙位龈正中注射脂多糖建立的SD大鼠实验性牙槽骨吸收作为体内药效试验模型，通过组织切片形态学骨密度观察和抗酒石酸酸性磷酸酶染色评价上述药物对大鼠实验性牙槽骨吸收的疗效.结果发现：1 mg • L-1水提液，0.01 mg • L-1和1 mg • L-1醇提液能显著促进MC3T3-E1细胞增殖.各浓度的水提液和醇提液均能使S期细胞百分率升高，G1期细胞百分率减少.0.01 mg• L-1水提液和1 mg• L-1醇提液作用于细胞的第1~3 d内、100 mg • L-1醇提液作用第7~9 d内、100 mg• L-1水提液作用第10~12 d内可显著提高细胞骨钙素合成和分泌.0.01 mg• L-1水提液和醇提液，1 mg• L-1水提液可显著促进细胞体外基质矿化.地龙水提液和醇提液组的骨密度均有显著增高，破骨细胞数显著降低.阴性对照组至第30 d时牙槽骨仍可见大量破骨细胞.结果表明：地龙水相和醇相提取物中均存在较高活性的促成骨细胞增殖、分化和基质矿化的物质，且对实验性牙槽骨吸收模型有一定治疗作用，可抑制其牙槽骨吸收，促进成骨活动使其修复重建.

Copyright of Journal of Synthetic Crystals is the property of Journal of Synthetic Crystals Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

脂质代谢异常是结直肠癌发病中最显著的代谢变化之一。脂质代谢的最终产物形成信号分子的结构基础, 促进肿瘤生长和新生血管形成。多项研究表明, 脂肪酸、甘油脂、胆固醇和鞘脂代谢异常与结直肠癌的发病有关。针对脂质代谢异常在结直肠癌发病中的上述作用, 对脂质代谢过程中的相关酶和蛋白进行调节, 如抑制脂肪酸合成酶、抑制柠檬酸裂解酶、抑制甾醇调节元件结合蛋白等, 或可为结直肠癌治疗提供新思路。 [ABSTRACT FROM AUTHOR]

Copyright of Forest Engineering is the property of Forest Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Clinical Healthcare is the property of Chinese Journal of Clinical Healthcare and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Clinical Healthcare is the property of Chinese Journal of Clinical Healthcare and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

While collaborative filtering(CF) algorithm shave been widely applied in recommender systems, the sparsity of the target rating data issue was still a crucial bottleneck for most existing CF methods. To this end, this paper proposed a novel CF algorithm-----LMTCF, to address the sparse collaborative filtering problem. In certain optimal latent subspace, LMTCF aimed to reduce the sparsity in target data by transferring knowledge (also called as the latent factors of users and items) from multiple dense auxiliary data sources as well as preserving the local geometrical structure of the target data. Besides, LMTCF could additionally address two key issues effectively, i. e. , negative transfer and inadequate transfer learning, thus allowing more positive knowledge transferred across domains to reduce the sparsity of target data. Experiments on two benchmark datasets demonstrate that this method significantly outperforms state-of-the-art CF methods. [ABSTRACT FROM AUTHOR]