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Introduction: Opinion mining has been receiving increasing attention from a broad range of scientific communities since early 2000s. The present study aims to systematically investigate the intellectual structure of opinion mining research. Method: Using topic search, citation expansion, and patent search, we collected 5,596 bibliographic records of opinion mining research. Then, intellectual landscapes, emerging trends, and recent developments were identified. We also captured domain-level citation trends, subject category assignment, keyword co-occurrence, document co-citation network, and landmark articles. Analysis: Our study was guided by scientometric approaches implemented in CiteSpace, a visual analytic system based on networks of co-cited documents. We also employed a dual-map overlay technique to investigate epistemological characteristics of the domain. Results: We found that the investigation of algorithmic and linguistic aspects of opinion mining has been of the community's greatest interest to understand, quantify, and apply the sentiment orientation of texts. Recent thematic trends reveal that practical applications of opinion mining such as the prediction of market value and investigation of social aspects of product feedback have received increasing attention from the community. Conclusion: Opinion mining is fast-growing and still developing, exploring the refinements of related techniques and applications in a variety of domains. We plan to apply the proposed analytics to more diverse domains and comprehensive publication materials to gain more generalized understanding of the true structure of a science.

The incomplete prediction of prognosis in esophageal squamous cell carcinoma (ESCC) patients is attributed to various therapeutic interventions and complex prognostic factors. Consequently, there is a pressing demand for enhanced predictive biomarkers that can facilitate clinical management and treatment decisions. This study recruited 491 ESCC patients who underwent surgical treatment at Huashan Hospital, Fudan University. We incorporated 14 blood metabolic indicators and identified independent prognostic indicators for overall survival through univariate and multivariate analyses. Subsequently, a metabolism score formula was established based on the biochemical markers. We constructed a nomogram and machine learning models utilizing the metabolism score and clinically significant prognostic features, followed by an evaluation of their predictive accuracy and performance. We identified alkaline phosphatase, free fatty acids, homocysteine, lactate dehydrogenase, and triglycerides as independent prognostic indicators for ESCC. Subsequently, based on these five indicators, we established a metabolism score that serves as an independent prognostic factor in ESCC patients. By utilizing this metabolism score in conjunction with clinical features, a nomogram can precisely predict the prognosis of ESCC patients, achieving an area under the curve (AUC) of 0.89. The random forest (RF) model showed superior predictive ability (AUC = 0.90, accuracy = 86%, Matthews correlation coefficient = 0.55). Finally, we used an RF model with optimal performance to establish an online predictive tool. The metabolism score developed in this study serves as an independent prognostic indicator for ESCC patients. [ABSTRACT FROM AUTHOR]

BACKGROUND: Numerous studies have confirmed that stimulating the mid-brain motor nuclei can regulate movement forcibly for robo-pigeons, but research on behavior modulation using non-motor nuclei is scarce. OBJECTIVE: In this study, we constructed a spatial preference behavior by stimulating the stratum griseum periventriculare (SGP), a nucleus correlated with fear and escape, for robo-pigeons. METHODS: The study was carried out in a square-enclosed experimental field, with a designated box serving as the 'safe' area for the robo-pigeons. If the robo-pigeon exits this area, the SGP will be stimulated. After a brief training period, the robo-pigeons will have a clear spatial preference for the box. RESULTS: The result from five pigeons has shown that, after simple training, the animals develop a spatial preference for the box. They can quickly return to the box in any situation when the SGP is stimulated, with a success rate exceeding 80% (89.0 ± 6.5%). Moreover, this behavior is highly stable and remains consistent, unaffected by changes in the location of the box or the interference box. CONCLUSION: The results prove that using the electrical stimulus could enable animals to accomplish more complex tasks. It may offer a novel approach to regulating pigeon behavior and further advance the study of cyborg animals. [ABSTRACT FROM AUTHOR]

Studying the association between microbes and diseases not only aids in the prevention and diagnosis of diseases, but also provides crucial theoretical support for new drug development and personalized treatment. Due to the time‐consuming and costly nature of laboratory‐based biological tests to confirm the relationship between microbes and diseases, there is an urgent need for innovative computational frameworks to anticipate new associations between microbes and diseases. Here, we propose a novel computational approach based on a dual branch graph convolutional network (GCN) module, abbreviated as DBGCNMDA, for identifying microbe–disease associations. First, DBGCNMDA calculates the similarity matrix of diseases and microbes by integrating functional similarity and Gaussian association spectrum kernel (GAPK) similarity. Then, semantic information from different biological networks is extracted by two GCN modules from different perspectives. Finally, the scores of microbe–disease associations are predicted based on the extracted features. The main innovation of this method lies in the use of two types of information for microbe/disease similarity assessment. Additionally, we extend the disease nodes to address the issue of insufficient features due to low data dimensionality. We optimize the connectivity between the homogeneous entities using random walk with restart (RWR), and then use the optimized similarity matrix as the initial feature matrix. In terms of network understanding, we design a dual branch GCN module, namely GlobalGCN and LocalGCN, to fine‐tune node representations by introducing side information, including homologous neighbour nodes. We evaluate the accuracy of the DBGCNMDA model using five‐fold cross‐validation (5‐fold‐CV) technique. The results show that the area under the receiver operating characteristic curve (AUC) and area under the precision versus recall curve (AUPR) of the DBGCNMDA model in the 5‐fold‐CV are 0.9559 and 0.9630, respectively. The results from the case studies using published experimental data confirm a significant number of predicted associations, indicating that DBGCNMDA is an effective tool for predicting potential microbe–disease associations. [ABSTRACT FROM AUTHOR]

Stalking, a widespread and distressing phenomenon, has recently garnered considerable attention. The advent of digital platforms has revolutionized the landscape of stalking, presenting new avenues and challenges for research. However, the impact of the coronavirus disease (COVID)-19 pandemic on stalking remains underexplored, despite extensive studies on similar crimes such as intimate partner violence and domestic violence. To address this gap, our study focused on Reddit, a prominent online platform with a diverse user base and open discussion. Through an analysis of posts from the subreddit (https://www.reddit.com/r/Stalking/), we sought to compare the discourse on stalking before and after the COVID-19 pandemic. We found notable shifts in stalking-related posts before and after the COVID-19 pandemic, particularly with the emergence of new topics centered on cyberstalking. We also observed that the experiences of stalking victims have significantly changed following the COVID-19 pandemic. Based on our findings, we discussed the implications for policies to help stalking victims. [ABSTRACT FROM AUTHOR]