Three kinds of magnetic particle (water-based NiZn ferrite fluid, water-based Fe3O4 magnetic fluid, and silicon-oil-based Fe3O4 magnetic fluid)/polyaniline nanocomposites were prepared in this study. The samples, after drying and grinding, were characterized by infrared spectrometry (IR), X-ray diffraction (XRD), and UV-vis, scanning electron microscope (SEM); their electromagnetic properties were also measured. The conductivitiy of the resulting water-based NiZn ferrite/polyaniline nanocomposites (WBNiZnFe/PA) was the greatest, reaching 0.094 s/cm, while the conductivitiy for water-based Fe3O4 magnetic particle/polyaniline nanocomposites (WBFe3O4/PA) was the lowest, reaching only 0.068 s/cm. The saturation magnetization for WBFe3O4/PA was the greatest, being 1.5 emu/g, while the saturation magnetization for WBNiZnFe/PA was the lowest, being only 0.8 emu/g. The coercivity of all magnetic particle/polyaniline nanocomposites was about He = 200 Oe.
As we all known, there is still a long way for us to solve arbitrary multivariate Lagrange interpolation in theory. Nevertheless, it is well accepted that theories about Lagrange interpolation on special point sets should cast important lights on the general solution. In this paper, we propose a new type of bivariate point sets, quasi-tower sets, whose geometry is more natural than some known point sets such as cartesian sets and tower sets. For bivariate Lagrange interpolation on quasi-tower sets, we construct the associated degree reducing interpolation monomial and Newton bases w.r.t. common monomial orderings theoretically. Moreover, by inputting these bases into Buchberger-Möller algorithm, we obtain the reduced Gröbner bases for vanishing ideals of quasi-tower sets much more efficiently than before.
In recent years, the research on the combination of linguistics and translatology is becoming more and more abundant.This study not only introduces the study of the two subjects, but also explores the problems of gaps in translation process of ancient Chinese poetry.Through the approach of contrastive research, this study analyses the contrast of lexeme and semantic gaps reflected by nouns in ten English versions of Li Bai's poem: Seeing Meng Haoran off at Yellow Crane Tower.Results show that the social and cultural factors behind the naming of nouns are closely related to the emergence of these gaps.Translators should find out exact causes behind those gaps and choose appropriate translation strategies according to the different types of the gaps.
Short-term load forecasting (STLF) is an important but a difficult task due to the uncertainty and complexity of electric power systems. In recent times, an attention-based model, Informer, has been proposed for efficient feature learning of lone sequences. To solve the quadratic complexity of traditional method, this model designs what is called ProbSparse self-attention mechanism. However, this mechanism may neglect daily-cycle property of load profiles, affecting its performance of STLF. To solve this problem, this study proposes an improved Informer model for STLF by considering the periodic property of load profiles. The improved model concatenates the output of Informer, the periodic load values of input sequences, and outputs forecasting results through a fully connected layer. This makes the improved model could not only inherit the superior ability of the traditional model for the feature learning of long sequences, but also extract periodic features of load profiles. The experimental results on three public data sets showed its superior performance than the traditional Informer model and others for STLF.
Abstract Matrix stiffness can have significant effects on cell behavior, regulating processes such as proliferation, differentiation, migration, and extracellular matrix production; however, less is known regarding the epigenomic and transcriptional regulation underling the effect of matrix stiffness on cell phenotypic shifts. In the present study, we utilized an in vitro system to assess the phenotypic shifts of hepatic stellate cells (HSCs) following changes in matrix stiffness, in addition to integrating multi-omics with imaging and biochemical assays to investigate the mechanism underlying the effect of mechanical stimuli on fibrosis. We show that cells cultured on a stiff matrix display more accessible chromatin sites, which consist of primed chromatin regions that become more accessible prior to the upregulation of nearby genes. These regions are enriched in fibrosis-associated genes that function in cytoskeletal organization and response to mechanical stimuli. Mechanistically, we demonstrate that activation of the AP-1 transcription factor family is responsible for chromatin priming, among which activated p-JUN is critical for the promotion of fibrogenic phenotypic shifts. The identified chromatin accessibility-dependent effect of matrix stiffness on cellular phenotypic shifts may be responsible for various fibrotic diseases and provide insight into intervening approaches.
Abstract A metagenome contains all DNA sequences from an environmental sample, including viruses, bacteria, fungi, actinomycetes and so on. Since viruses are of huge abundance and have caused vast mortality and morbidity to human society in history as a kind of major pathogens, detecting viruses from metagenomes plays a crucial role in analysing the viral component of samples and is the very first step for clinical diagnosis. However, detecting viral fragments directly from the metagenomes is still a tough issue because of the existence of huge number of short sequences. In this paper, a hybrid Deep lEarning model for idenTifying vIral sequences fRom mEtagenomes (DETIRE), is proposed to solve the problem. Firstly, the graph-based nucleotide sequence embedding strategy is utilized to enrich the expression of DNA sequences by training an embedding matrix. Then the spatial and sequential features are extracted by trained CNN and BiLSTM networks respectively to improve the feature expression of short sequences. Finally, the two set of features are weighted combined for the final decision. Trained by 220,000 sequences of 500bp subsampled from the Virus and Host RefSeq genomes, DETIRE identifies more short viral sequences (<1,000bp) than three latest methods, DeepVirFinder, PPR-Meta and CHEER. DETIRE is freely available at https://github.com/crazyinter/DETIRE .
To evaluate the soil quality in the main cotton growing regions of Xinjiang, 11 soil quality indices were measured in representative locations: Hami, Bole, Changji, Shihezi, Aksu, Kashgar and Kuitun. The indices included soil pH, salt, organic matter, total N, available P and available K for soil physicochemical properties, and Cr, Cu, Zn, As, and Pb for soil heavy metal pollution. Based on these indices, a comprehensive soil quality index (SQI) was developed to analyze the soil quality in the cotton fields of Xinjiang. The results showed that the soils in Xinjiang's cotton fields were alkaline, with an average pH of 7.87. The soils were mildly saline, with an average salt content of 3.44 g·kg-1. Soil organic matter and total N concentrations were generally low, whereas available P and available K concentrations were relatively high. Soil available P concentrations were significantly higher than that of the second national soil survey, whereas soil pH, salt content, organic matter, and total N were less. Soil available K was greater in some regions but lower in others compared with the second national soil survey. The average heavy metal concentrations were as follows: Cr, 45.88 mg·kg-1; Cu, 40.66 mg·kg-1; Zn, 68.30 mg·kg-1; As, 12.88 mg·kg-1; and Pb, 16.68 mg·kg-1. These values were all below the national standards. However, the Cu, Zn, and As concentrations were greater than the background values in Xinjiang, indicating the accumulation of those elements in the soils. When the Nemerow comprehensive pollution index (PN) of heavy metals was less than 0.5, the comprehensive soil quality improved as the soil physicochemical properties improved. Soil organic matter, total N, Cu, Zn, As and Pb were the main variables affecting soil quality in the main cotton cropping regions of Xinjiang. The cotton growing areas in Xinjiang generally had medium soil quality. Changji and Kuitun had the highest SQI (0.52) whereas Aksu had the lowest value (0.31). Soil quality was generally highest in northern Xinjiang, followed by western area, and then southern area.以新疆主要棉区为研究对象,测定了哈密、博乐、昌吉、奎屯、石河子、阿克苏及喀什棉田土壤耕层的pH、盐分、有机质、全氮、速效磷、速效钾及Cr、Cu、Zn、As、Pb 共计11个指标,综合分析土壤理化性质和重金属含量,采用土壤质量综合指数(SQI)对新疆主要棉区棉田土壤质量进行综合评价.结果表明: 新疆棉区棉田土壤呈碱性,pH均值为7.87,盐分含量均值为3.44 g·kg-1,为轻度盐化土壤,有机质和全氮含量均偏低,速效磷、速效钾含量较为丰富,与第二次全国土壤普查数据相比,土壤pH、盐分含量、有机质和全氮均呈下降趋势,土壤速效磷明显增长,部分地区土壤速效钾呈现出不同程度的升高趋势;Cr、Cu、Zn、As、Pb 5种重金属含量分别为45.88、40.66、68.30、12.88、16.68 mg·kg-1,均未超过国家二级标准,但与新疆土壤元素背景值相比,Cu、Zn、As均有累积现象.当重金属内梅罗综合污染指数(PN)小于0.5时,土壤理化性质越好,土壤综合质量越好.土壤有机质、全氮、Cu、Zn和As是影响新疆棉区棉田土壤质量的重要因素.新疆棉区棉田土壤质量总体属于中等水平,昌吉、奎屯质量最高,SQI为0.52,阿克苏质量最低,SQI为0.31,不同棉区土壤质量呈现为:北疆>东疆>南疆.
Metagenome sequencing provides an unprecedented opportunity for the discovery of unknown microbes and viruses. A large number of phages and prokaryotes are mixed together in metagenomes. To study the influence of phages on human bodies and environments, it is of great significance to isolate phages from metagenomes. However, it is difficult to identify novel phages because of the diversity of their sequences and the frequent presence of short contigs in metagenomes. Here, virSearcher is developed to identify phages from metagenomes by combining the convolutional neural network (CNN) and the gene information of input sequences. Firstly, an input sequence is encoded in accordance with the different functions of its coding and the non-coding regions and then is converted into word embedding code through a word embedding layer before a convolutional layer. Meanwhile, the hit ratio of the virus genes is combined with the output of the CNN to further improve the performance of the network. The genes used by virSearcher consist of complete and incomplete genes. Experiments on several metagenomes have showed that, compared with others, virSearcher can significantly improve the performance for the identification of short sequences, while maintaining the performance for long ones. The source code of virSearcher is freely available from http://github.com/DrJackson18/virSearcher.
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