Synthesis and Biological Evaluation of Ezetimibe Analogs as Possible Cholesterol Absorption Inhibitors

In order to investigate the SAR of Ezetimibe analogs for cholesterol absorption inhibitions, amide group and electron-deficient pyridine ring were introduced to the C-(3) carbon chain of Ezetimibe. Eight new derivatives of the 2-azetidinone cholesterol absorption inhibitors have been synthesized, and all of them were enantiomerically pure. All the new compounds were evaluated for their activity to inhibit cholesterol absorption in hamsters, and most of them showed comparable effects in lowering the levels of total cholesterol in the serum

Continuous intuitionistic fuzzy ordered weighted distance measure and its application to group decision making

The aim of this paper is to develop the continuous intuitionistic fuzzy ordered weighted distance (C-IFOWD) measure by using the continuous intuitionistic fuzzy ordered weighted averaging (C-IFOWA) operator in the interval distance. We investigate some desirable properties and different families of the C-IFOWD measure. We also generalize the C-IFOWD measure. The prominent characteristics of the C-IFOWD measure are that it is not only a generalization of some widely used distance measure, but also it can deal with interval deviations in aggregation on interval-valued intuitionistic fuzzy values (IVIFVs) by using a controlled parameter, which can decrease the uncertainty of argument and improve the accuracy of decision. The desirable characteristics make the C-IFOWD measure suitable to wide range situations, such as decision making, engineering and investment, etc. In the end, we introduce a new approach to group decision making with IVIFVs in human resource management. First published online: 18 Sep 201

Distribution Linguistic Fuzzy Group Decision Making Based on Consistency and Consensus Analysis

The development of distribution linguistic provides a new research idea for linguistic information group decision-making (GDM) problems, which is more flexible and convenient for experts to express their opinions. However, in the process of using distribution linguistic fuzzy preference relations (DLFPRs) to solve linguistic information GDM problems, there are few studies that pay attention to both internal consistency adjustment and external consensus of experts. Therefore, this study proposes a fresh decision support model based on consistency adjustment algorithm and consensus adjustment algorithm to solve GDM problems with distribution linguistic data. Firstly, we review the concept of DLFPRs to describe the fuzzy linguistic evaluation information, and then we present the multiplicative consistency of DLFPRs and a new consistency measurement method based on the distance, and investigate the consistency adjustment algorithm to ameliorate the consistency level of DLFPRs. Subsequently, the consensus degree measurement is carried out, and a new consensus degree calculation method is put forward. At the same time, the consensus degree adjustment is taken the expert cost into account to make it reach the predetermined level. Finally, a distribution linguistic fuzzy group decision making (DLFGDM) method is designed to integrate the evaluation linguistic elements and obtain the final evaluation information. A case of the evaluation of China’s state-owned enterprise equity incentive model is provided, and the validity and superiority of the proposed method are performed by comparative analysis

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Data from: Small RNA sequencing reveals a novel tsRNA-26576 mediating tumorigenesis of breast cancer

Purpose: As a malignancy that develops from breast tissue, breast cancer has been widely regarded as the most common type of cancer threatening the health of women worldwide. Emerging evidence has demonstrated that tsRNAs might play a vital part in the tumorigenesis and progression of several types of cancers. However, the functions of tsRNAs in breast cancer remain largely unknown. Here, we investigated the functions of tsRNA-26576 in tumorigenesis of breast cancer. Patients and methods: In this study, the tsRNA deregulation states in breast cancer patients (four cancer tissues and four adjacent normal tissues) were evaluated using small RNA sequencing. And then, RT-PCR was used to detected the tsRNA-26576 expression level in breast cancer patients. Results: A total of 263 tsRNAs were identified as significantly differentially expressed, of which 75 were upregulated, and 188 were downregulated. The functional classification through KEGG pathway database illustrated that the most significant pathway enriched by the targets of differentially expressed tsRNAs was the pathway in cancer. Among these differently expressed tsRNAs, we found that tsRNA-26576 was remarkably upregulated in cancer tissue in comparison with adjacent normal tissue. Meanwhile, RT-PCR results verified that tsRNA-26576 expression level was highly upregulated in 10 paired samples from breast cancer patients. Besides, tsRNA-26576 was found to motivate cellular multiplication and migration while suppressing cellular apoptosis in MDA-MB-231 cells. Moreover, mRNA sequencing results showed that several tumor suppressor genes, including FAT4 and SPEN, were upregulated after delivering tsRNA-26576 inhibitor in MDA-MB-231 cells. Conclusion: We found tsRNA-26576 was upregulated in breast cancer tissue, and it could promote the cell growth while inhibite cell apoptosis. Therefore, tsRNA-26576 might serve as a potential clinical therapy target and a predictive marker for breast cancer

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GNSS Signal Compression Acquisition Algorithm Based on Sensing Matrix Optimization

Due to the sparsity of GNSS signal in the correlation domain, compressed sensing theory is considered to be a promising technology for GNSS signal acquisition. However, the detection probability of the traditional compression acquisition algorithm is low under low signal-to-noise ratio (SNR) conditions. This paper proposes a GNSS compression acquisition algorithm based on sensing matrix optimization. The Frobenius norm of the difference between Gram matrix and an approximate equiangular tight frame (ETF) matrix is taken as the objective function, and the modified conjugate gradient method is adopted to reduce the mutual coherence between the measurement matrix and the sparse basis. Theoretical analysis and simulation results show that the proposed algorithm can significantly improve the detection probability compared with the existing compression acquisition algorithms under the same SNR