A post processing strategy to score and rank the annotation confidence of saponins in natural products by integrating MS2 spectral similarity and fragment interpretation

2021 ◽  
pp. 114291
Author(s):  
Tong Xie ◽  
Jinjun Shan ◽  
Jun Jiang ◽  
Xia Zhao ◽  
Yu He ◽  
...  
Keyword(s):  
Natural Products ◽  
Processing Strategy ◽  
Post Processing ◽  
Spectral Similarity

scholarly journals Estratégias para Aprimorar a Diversidade Categórica e Geográfica de Sistemas de Recomendação de POIs

2020 ◽  
Author(s):  
Rodrigo Carvalho ◽  
Leonardo Rocha
Keyword(s):  
Social Networks ◽  
User Satisfaction ◽  
Recommendation Systems ◽  
Experimental Results ◽  
Data Sets ◽  
Processing Strategy ◽  
Post Processing ◽  
Practical Utility ◽  
Points Of Interest ◽  
Location Based Social Networks

Currently, so-called Recommendation Systems (SRs) have been used to assist users in discovering relevant Points of Interest (POIs) on Location-Based Social Networks (LBSN), such as FourSquare and Yelp. Given the main challenges of data-sparse and geographic influence in this scenario, most of the work on POI recommendations has focused only on improving the effectiveness (i.e. accuracy) of the systems. However, there is a growing consensus that just effectiveness is not sufficient to assess the practical utility of these systems. In real scenarios, categorical and geographic diversities were identified as the main complementary dimensions for assessing user satisfaction and the usefulness of recommendations. The works in the literature are concentrated on only one of these concepts. In this work, we propose a new post-processing strategy, which combines these concepts in order to improve the user’s interest in POIs. Our experimental results in the Yelp data sets show that our strategy can improve user satisfaction, considering different SRs and multiple diversification metrics. Our method is capable of improving diversity by up to 120 % without significant losses in terms of effectiveness.

Processing terminology for the translation pipeline

Terminology ◽  
2013 ◽  
Vol 19 (1) ◽  
pp. 93-111 ◽  
Author(s):  
Kara Warburton
Keyword(s):  
Market Share ◽  
Effective Practices ◽  
Target Market ◽  
Processing Strategy ◽  
Post Processing ◽  
Key Factors ◽  
Lexical Resources ◽  
Beneficial Effects ◽  
Term Extraction ◽  
Key Terms

Companies must translate their content if they want to operate multinationally. Both quality and speed of translation are key factors in determining market share in the target market. Proactively managing terminology, including pre-translating key terms for a translation project, has beneficial effects on these factors. However, in commercial environments, the volumes of content and subsequently of the required terminology are typically large. Therefore, integrating terminology into the translation pipeline requires a process that is as automated as possible. Term extraction is the cornerstone of this process, but to maximize efficiency it requires a post-processing strategy that repurposes existing lexical resources. Terms extracted from corpora and subsequently translated should be channeled into the company termbase so that they can be leveraged for other purposes. These and other effective practices for processing extracted terms are discussed, based on the author’s experiences in one large company.

scholarly journals REDES MOLECULARES: UMA ANÁLISE SOBRE ANOTAÇÕES E DESCOBERTA DE NOVOS ATIVOS

Química Nova ◽  
2021 ◽  
Author(s):  
Alan Pilon ◽  
Natália Vieira ◽  
Juliano Amaral ◽  
Afif Monteiro ◽  
Ricardo Silva ◽  
...  
Keyword(s):  
Natural Products ◽  
Large Data ◽  
Large Data Sets ◽  
Molecular Networks ◽  
Data Sets ◽  
Spectral Similarity ◽  
Bioactive Natural Products ◽  
Speed Up ◽  
Similarity Networks

MOLECULAR NETWORKS: AN ANALYSIS ON ANNOTATIONS AND DISCOVERY OF NEW ASSETS. To speed up the discovery of bioactive natural products (NP), chemists have sought advanced approaches in analytical and computational chemistry in attempt to organize and extract information from large data sets. In this sense, the molecular networks (MN) successfully organized enormous sets of mass spectrometry (MS) data together with samples metadata information in an intuitive visualization in the spectral similarity networks format. GNPS (Global Natural Products Social Molecular Networking), a free online platform for storing and processing MSn data, is a leading application of spectral matching with public databases aimed at the dereplication and discovery of new bioactive products through molecular networks. In this review, we address the concept of GNPS spectral similarity networks, as well as their complementary computational tools, benefits and limitations applied in NP studies associated with dereplication, chemical ecology, functional genetics and determination of biosynthetic pathways.

Processing of Underground Nuclear Magnetic Resonance Data for Underground River Detection: A Case Study in Doumo Tunnel, Guizhou, China

2020 ◽  
Vol 25 (3) ◽  
pp. 315-323
Author(s):  
Yang Zhang ◽  
Sijia Yu ◽  
Ling Wan ◽  
Tingting Lin
Keyword(s):  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Noise Level ◽  
Processing Strategy ◽  
Post Processing ◽  
Underground Engineering ◽  
Time Frequency ◽  
Nmr Data ◽  
Underground River ◽  
Nuclear Magnetic

Underground nuclear magnetic resonance (NMR) is introduced to detect the risk of groundwater-induced disasters in the underground engineering such as tunnels and mines. However, underground NMR is in practice often limited to the extremely low signal-to-noise ratios (SNRs). On the one hand, small coils are necessary to be used to detect water in the narrow underground space, which decreases the amplitude of the excited signal. On the other hand, the weak signal is submerged in quite serious electromagnetic noise which is generated from the electrical installations. The low SNRs emphasize the importance of using an optimal post-processing strategy to obtain the reliable underground NMR data. The objective of this paper is to explain the processing of underground NMR data taking the detection of the underground river in Doumo Tunnel as an example. We have evaluated the noise condition in Doumo Tunnel and the noise level of 0.6760 nV/m2 was found in this area. At such a high noise level, the reliable underground NMR signal is difficult to be extracted and the credible depth profile of water content is unable to be provided. Then, we have analyzed the noise interference. Although de-spiking algorithm and reference-based noise cancellation method were applied to remove the major noise sources, the underground NMR signal is still invisible. There is still a lot of additive noise remained, so time-frequency peak filtering method is further used to suppress the remaining noise. The performance of the proposed post-processing strategy is tested on the underground NMR data from the underground river. The result was consistent with the geological structure, which is demonstrated to be able to directly provide a security pre-warning of the underground engineering.

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