Alzheimer's disease (AD), which is the most common dementia, has become a critical social and medical problem. High throughput microarray technology make it feasible to have a view at the molecular level. Identifying potential genes at the molecular level which are discriminatory in AD affected versus healthy controls will greatly help understand the pathogenesis of AD as well as the diagnosis and treatment of AD. In this article, we propose a new criterion to evaluate the importance of gene pairs by combining both latitudinal comparison and longitudinal comparison and then select discriminatory gene pairs for AD in four different brain regions: hippocampus (HC), entorhinal cortex (EC), superior frontal gyrus (SFG), and post-central gyrus (PCG). The experiment results show that the suggested criterion performs well in all four brain regions and the selected discriminatory gene pairs can help deep understand the pathogenesis of AD.
To determine whether gut microbiota, fatty metabolism and cytokines were associated with immune thrombocytopenia (ITP).In total, 29 preliminarily diagnosed ITP patients and 33 healthy volunteers were enrolled. Fecal bacterial were analyzed based on 16S rRNA sequencing. Plasma cytokines and motabolites were analyzed using flow cytometry and liquid chromatography-mass spectrometry (LC-MS), respectively.Bacteroides, Phascolarctobacterium, and Lactobacillus were enriched at the genus level in ITP patients, while Ruminococcaceae UCG-002, Eubacterium coprostanoligeues, Megamonas, and Lachnospiraceae NC2004 were depleted. At the phylum level, the relative abundance of Proteobacteria and Chloroflexi increased in ITP patients, while Firmicutes, Actinobacteria, and the Firmicutes/Bacteroidetes ratio decreased. Plasma levels of 5-hydroxyeicosatetraenoic acid (5-HETE), 6-trans-12-epi-leukotriene B4 (6t,12e-LTB4), and resolvin D2 (RvD2) were upregulated, and stachydrine, dowicide A, dodecanoylcarnitine were downregulated in ITP patients. Furthermore, RvD2 is positively correlated with order Bacteroidetes VC2.1 Bac22, 5-HETE is positively correlated with genus Azospirillum, and 6t,12e-LTB4 is positively correlated with genus Cupriavidus. In addition, stachydrine is positively correlated with family Planococcaceae, dowicide A is positively correlated with class MVP-15, and dodecanoylcarnitine is positively correlated with order WCHB1-41. Plasma levels of interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) were upregulated in ITP patients.Our study revealed a relationship between microbiota and fatty metabolism in ITP. Gut microbiota may participate in the pathogenesis of ITP through affecting cytokine secretion, interfering with fatty metabolism.
The LSTM network was proposed to overcome the difficulty in learning long-term dependence, and has made significant advancements in applications. With its success and drawbacks in mind, this paper raises the question - do RNN and LSTM have long memory? We answer it partially by proving that RNN and LSTM do not have long memory from a statistical perspective. A new definition for long memory networks is further introduced, and it requires the model weights to decay at a polynomial rate. To verify our theory, we convert RNN and LSTM into long memory networks by making a minimal modification, and their superiority is illustrated in modeling long-term dependence of various datasets.
In this paper a reduction algorithm based on rough set theory is presented due to too much factors that influence accuracy in the power load forecasting. The reduction algorithm introduced to mine more correlative attributes in the pending forecasting components, ensures not only the rationality of input parameters of forecasting model but also the selection of input parameters of ANN model. An RAPHF (reduction algorithm through prior heuristic function) algorithm based on attributes-prior algorithm is introduced because reduction Algorithm based on dipartite matrix reduction algorithm is a NP-hard problem. On the basis of RAPHF, a rough set incremental algorithm with dynamic mining ability, namely, RAPHF-I is proposed when considering the updating samples. The efficiency and advantage of our method is proved by prediction results of short-term load based on the RAPFF and RAPHF-I.
The second boiler is the equipment of Dongfang Boiler Company in a thermal power plant,which model is DG1069 /17. 4-Ⅱ1. The bed temperature distribution is higher in the middle than both sides during the boiler operation. To solve the problem,carried out the operating data analysis,the improvement program of nozzles and the effectiveness analysis of the program,etc. The data comparison shows that the bed temperature difference has been significantly improved in most conditions,while there is a small bed resistance increasing after the transformation. This paper provides valuable test data for the energy saving of similar CFB boilers.
TFIDF is a kind of common methods used to measure the terms in a document.The method is easy but it undervalues these terms that frequently appear in the documents belonging to the same class,while those terms can represent the characteristic of the documents of this class,so higher weight is entrusted to them.The expression of IDF in TFIDF is modified to increase the weight of those terms mentioned,then is applied to the experiment to validate it.In the experiment,the improved TFIDF is used to select feature and genetic algorithm is used to train the classifier.The method is better than others and proves that the improved TFIDF method is feasible.
This study aimed to extract polysaccharides from Citrus medica L. var. sarcodactylis (finger citron fruits) and analyze their structures and potential bioactivities. A new polysaccharide named K-CMLP was isolated and purified by Diethylaminoethylcellulose (DEAE)-Sepharose Fast Flow and DEAE-52 cellulose column chromatography with an average molecular weight of 3.76 × 10 3 kDa. Monosaccharide composition analysis revealed that K-CLMP consisted of rhamnose, galactose, and glucose, with a molar ratio of 6.75:5.87:1.00. Co-resolved by methylation and two-dimensional nuclear magnetic resonance (NMR), K-CLMP was alternately connected with 1, 2-Rha and 1, 4-Gal to form the backbone, and a small number of glucose residues was connected to O-4 of rhamnose. The results of DPPH⋅ and ABTS + ⋅ radical scavenging assays indicated that both crude polysaccharide Citrus medica L. var. polysaccharide (CMLP) and K-CLMP exhibited strong free-radical-scavenging properties in a dose-dependent manner. In addition, K-CMLP significantly inhibited the production of pro-inflammatory cytokines (IL-6 and TNF-α) and reactive oxygen species (ROS) in RAW 264.7 cells treated with LPS. These results provide a basis for further use as one of the potential functions of food or natural medicine.
Local learning has been successfully applied to transductive classification problems. In this paper, it is generalized to multi-class classification of transductive learning problems owing to its good classification ability. Meanwhile, there is essentially no ordinal meaning in class label of multi-class classification, and it belongs to discrete nominal variable. However, common binary series class label representation has the equal distance from one class to another, and it does not reflect the sparse and density relationship among classes distribution, so a learning and adjustable nominal class label representation method is presented. Experimental results on a set of benchmark multi-class datasets show the superiority of our algorithm.
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