Background Gastrointestinal microbiome has drawn an increasing amount of attention over the past decades. There is emerging evidence that the gut flora plays a major role in the pathogenesis of certain diseases. We aimed to analyze the evolution of gastrointestinal microbiome research and evaluate publications qualitatively and quantitatively. Methods We obtained a record of 2891 manuscripts published between 1998 and 2018 from the Web of Science Core Collection (WoSCC) of Thomson Reuters; this record was obtained on June 23, 2018. The WoSCC is the most frequently used source of scientific information. We used the term “Gastrointestinal Microbiomes” and all of its hyponyms to retrieve the record, and restricted the subjects to gastroenterology and hepatology. We then derived a clustered network from 70,169 references that were cited by the 2891 manuscripts, and identified 676 top co-cited articles. Next, we used the bibliometric method, CiteSpace V, and VOSviewer 1.6.8 to identify top authors, journals, institutions, countries, keywords, co-cited articles, and trends. Results We identified that the number of publications on gastrointestinal microbiome is increasing over time. 112 journals published articles on gastrointestinal microbiome. The United States of America was the leading country for publications, and the leading institution was the University of North Carolina. Co-cited reference analysis revealed the top landmark articles in the field. Gut microbiota, inflammatory bowel disease (IBD), probiotics, irritable bowel disease, and obesity are some of the high frequency keywords in co-occurrence cluster analysis and co-cited reference cluster analysis; indicating gut microbiota and related digestive diseases remain the hotspots in gut microbiome research. Burst detection analysis of top keywords showed that bile acid, obesity, and Akkermansia muciniphila were the new research foci. Conclusions This study revealed that our understanding of the link between gastrointestinal microbiome and associated diseases has evolved dramatically over time. The emerging new therapeutic targets in gut microbiota would be the foci of future research.

Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging. For fast data acquisition or other inevitable reasons, however, only a small amount of samples may be acquired and thus how to recover the full signal becomes an active research topic. But existing approaches can not efficiently recover N -dimensional exponential signals with N ≥ 3. In this paper, we study the problem of recovering N -dimensional (particularly N ≥ 3) exponential signals from partial observations, and formulate this problem as a low-rank tensor completion problem with exponential factor vectors. The full signal is reconstructed by simultaneously exploiting the CANDECOMP/PARAFAC structure and the exponential structure of the associated factor vectors. The latter is promoted by minimizing an objective function involving the nuclear norm of Hankel matrices. Experimental results on simulated and real magnetic resonance spectroscopy data show that the proposed approach can successfully recover full signals from very limited samples and is robust to the estimated tensor rank.

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Low-intensity signal reconstruction is generally challenging in biological MRS and we provide a solution to this problem. The proposed method may be extended to recover signals that generally can be modeled as a sum of exponential functions in biomedical engineering applications, e.g., signal enhancement, feature extraction, and fast sampling.

Many signals are modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging. This paper studies the problem of recovering exponential signals from a random subset of samples. We exploit the Vandermonde structure of the Hankel matrix formed by the exponential signal and formulate signal recovery as Hankel matrix completion with Vandermonde factorization (HVaF). A numerical algorithm is developed to solve the proposed model and its sequence convergence is analyzed theoretically. Experiments on synthetic data demonstrate that HVaF succeeds over a wider regime than the state-of-the-art nuclear-normminimization-based Hankel matrix completion method, while has a less restriction on frequency separation than the state-of-the-art atomic norm minimization and fast iterative hard thresholding methods. The effectiveness of HVaF is further validated on biological magnetic resonance spectroscopy data.