A Multi-Attribute Decision Making for Investment Decision Based on D Numbers Methods

Investment decision is a traditional multi-attribute decision making (MADM) problem since it has many uncertainty factors and incomplete information such as investment value, cost, sales, etc. D numbers theory is a useful tool to deal with uncertainty factors and incomplete information. In this paper, interval number and D numbers theory are revealed in the uncertain factor and incomplete information of investment decision. The weights of uncertain factors are calculated using entropy weight method. Thus, a new MADM model for investment decision based on D numbers theory is proposed. Numerical example is used to illustrate the efficiency of the proposed method.

Effect of arsenite on the proteome of earthworms Eisenia fetida

Arsenic(As)is broadly distributed due to natural and anthropogenic sources,and it is toxic to organisms.This study aimed to investigate the proteomic response in earthworm exposed to As^(3+).Earthworms were exposed to As^(3+)in soil at 5-80 mg kg1,and samples were collected after 60 days exposure.Two-dimensional electrophoresis(2-DE)was used to separate the proteins in earthworm homogenate,then differentially expressed proteins(DEPs)were identified using MALDI-TOF/TOF-MS analysis.After 2-DE,36 DEPs were found and 24 of them were successfully identified.79.2%of DEPs were upregulated compared to the control group.The maximum fold change reached 53.8 in spot 3108 in the 80 mg kg^(-1)As group.Two proteins were not found in the control group but found in the As treated groups.Proteins were grouped into metabolism,signal transduction,stress-related,transport,regulation,and predicted/hypothetical protein categories based on their function.The protein-protein interaction between the DEPs indicated that serum albumin(ALB)is very important,related to 6 other proteins.Proteins were then verified by western blot,the results were in agreement with the proteomic analyses.The identification of induced or repressed proteins because of As^(3+)in earthworms is helpful to explore the underlying mechanisms of soil arsenic ecotoxicity.

Genome-wide identification of key regulatory lncRNAs in esophageal cancer metastasis

Dear Editor,Metastasis leads to a poor prognosis of patients with esophageal squamous cell carcinoma(ESCC).1-3 but the study on cancer metastasis has been hampered by a lack of reliable cell and animal models.Systematic identification and functional validation of metastasis-associated long non-coding RNAs(lncRNAs)and microRNAs(miRNAs),as well as-their interactions in ESCC are urgently needed.

Probability Loop Closure Detection with Fisher Kernel Framework for Visual SLAM

A typical approach to describe an image in loop closure detection for visual SLAM is to extract a set of local patch descriptors and encode them into a co-occurrence vector.The most common patch encoding strategy is known as bag-of-visual-words(BoVW)representation,which consists of clustering the local descriptors into visual vocabulary.The distinctiveness of images is difficult to represent since most of them contain similar texture information,which may lead to false positive results.In this paper,the vocabulary is used as a whole by adopting the Fisher kernel(FK)framework.The new representation describes the image as the gradient vector of the likelihood function.The efficiently computed vectors can be compressed with a minimal loss of accuracy using product quantization and perform well in the task of loop closure detection.The proposed method achieves a higher recall rate with 100%precision in loop closure detection compared with state-of-the-art methods,and the detection on bidirectional loops is also enhanced.vSLAM systems may perceive the environment more efficiently by constructing a globally consistent map with the proposed loop closure detection method,which is potentially valuable for applications such as autonomous driving.