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991.
Cross-lingual document retrieval, which aims to take a query in one language to retrieve relevant documents in another, has attracted strong research interest in the last decades. Most studies on this task start with cross-lingual comparisons at the word level and then represent documents via word embeddings, which leads to insufficient structure information. In this work, the cross-lingual comparison at the document level is achieved through the cross-lingual semantic space. Our method, MDL (deep multilabel multilingual document learning), leverages a six-layer fully connected network to project cross-lingual documents into a shared semantic space. The semantic distances can be calculated when the cross-lingual documents are transformed into embeddings in semantic space. The supervision signals are automatically extracted from the data and then used to construct the semantic space via a linear classifier. The ambiguity of manual labels could be avoided and the multilabel supervision signals can be acquired instead of a single label. The representation of the semantic space is enriched by multilabel supervision signals, which improves the discriminative ability of the embeddings. The MDL is easy to extend to other fields since it does not depend on specific data. Furthermore, MDL is more efficient than the models training all languages jointly, since each language is trained individually. Experiments on Wikipedia data showed that the proposed method outperforms the state-of-the-art cross-lingual document retrieval methods. 相似文献
992.
Siyu Liu Jie Liu Lan He Liu Liu Bo Cheng Fangliang Zhou Deliang Cao Yingchun He 《Molecules (Basel, Switzerland)》2022,27(14)
Curcumin is the most important active component in turmeric extracts. Curcumin, a natural monomer from plants has received a considerable attention as a dietary supplement, exhibiting evident activity in a wide range of human pathological conditions. In general, curcumin is beneficial to human health, demonstrating pharmacological activities of anti-inflammation and antioxidation, as well as antitumor and immune regulation activities. Curcumin also presents therapeutic potential in neurodegenerative, cardiovascular and cerebrovascular diseases. In this review article, we summarize the advancements made in recent years with respect to curcumin as a biologically active agent in malignant tumors, Alzheimer’s disease (AD), hematological diseases and viral infectious diseases. We also focus on problems associated with curcumin from basic research to clinical translation, such as its low solubility, leading to poor bioavailability, as well as the controversy surrounding the association between curcumin purity and effect. Through a review and summary of the clinical research on curcumin and case reports of adverse effects, we found that the clinical transformation of curcumin is not successful, and excessive intake of curcumin may have adverse effects on the kidneys, heart, liver, blood and immune system, which leads us to warn that curcumin has a long way to go from basic research to application transformation. 相似文献
993.
Feature selection (FS) is a vital step in data mining and machine learning, especially for analyzing the data in high-dimensional feature space. Gene expression data usually consist of a few samples characterized by high-dimensional feature space. As a result, they are not suitable to be processed by simple methods, such as the filter-based method. In this study, we propose a novel feature selection algorithm based on the Explosion Gravitation Field Algorithm, called EGFAFS. To reduce the dimensions of the feature space to acceptable dimensions, we constructed a recommended feature pool by a series of Random Forests based on the Gini index. Furthermore, by paying more attention to the features in the recommended feature pool, we can find the best subset more efficiently. To verify the performance of EGFAFS for FS, we tested EGFAFS on eight gene expression datasets compared with four heuristic-based FS methods (GA, PSO, SA, and DE) and four other FS methods (Boruta, HSICLasso, DNN-FS, and EGSG). The results show that EGFAFS has better performance for FS on gene expression data in terms of evaluation metrics, having more than the other eight FS algorithms. The genes selected by EGFAGS play an essential role in the differential co-expression network and some biological functions further demonstrate the success of EGFAFS for solving FS problems on gene expression data. 相似文献
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采用溶剂热法,1,3,5-三(羧基甲氧基)苯为定向配体和乙酸镍反应构筑了一个新型的金属配位聚合物[Ni(TB)2(H2O)2]n·2H2O,其中H3TB=1,3,5-三(羧基甲氧基)苯,通过元素分析、IR及X射线单晶衍射对配合物结构进行表征,并研究其荧光性质、热稳定性及Hirshfeld表面作用力。单晶结构分析表明,该配合物属于三斜晶系,空间群$P \overline{1}$,配合物中心离子Ni(Ⅱ)分别与来自两个水分子上的氧原子及四个不同1,3,5-三(羧基甲氧基)苯配体的羧酸氧原子配位,形成六配位的NiO6八面体构型,并通过与1,3,5-三(羧基甲氧基)苯配体的氧原子配位不断延伸形成具有孔洞结构的一维链状构型。配合物具有良好的荧光性能和热稳定性。Hirshfeld表面作用分析表明配合物分子中O…H/H…O作用占主导且占比为39.0%,而H…H的作用力占比为25.9%,O…O的作用力占比为13.6%。 相似文献
996.
电感耦合等离子体原子发射光谱法同时测定槲寄生中的微量元素 总被引:1,自引:0,他引:1
采用硝酸-双氧水对槲寄生全药及煎煮液进行湿法消解,用电感耦合等离子体原子发射光谱法(ICP-AES)同时测定试样中Cu,Mg,Zn,Mn,Ni,Se,K,Na,Ca和Fe等元素的含量。该法的的加标回收率为91.6%~108.9%,变异系数为0.47%~4.59%,具有良好的准确度和精密度。结果表明:槲寄生中含有丰富的K和Ca元素,Na和Mg的含量也相对丰富,为进一步研究槲寄生的药理提供了科学的数据。 相似文献
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采用联苯二酐与3种含酰胺结构二胺制备了具有不同取代基团的聚酰胺-酰亚胺薄膜,考察了酰胺结构对薄膜力学、耐热及尺寸稳定性的影响,研究了聚集态结构与薄膜热膨胀行为的关系和规律.该系列薄膜具有超高强度和优异的耐热性能,拉伸强度高达280. 5 MPa,玻璃化转变温度在389~409℃,并在30~300℃温度范围内表现出超低负膨胀,热膨胀系数(CTE,ppm/℃,即106cm·cm-1·℃-1)在-3. 05~-1. 74ppm/℃之间.聚集态分析结果表明,酰胺结构使分子链间形成了强氢键相互作用,分子链在薄膜面内方向高度有序取向,并在膜厚方向堆积更为紧密,使薄膜表现出热收缩现象.通过不同体积大小的取代基团进一步调控分子链间相互作用及排列堆积,可实现薄膜在高温下近乎零尺寸形变,为设计制备超低膨胀聚合物基板材料提供了新思路. 相似文献
1000.