Burst Detection Research Articles

Improved Type III solar radio burst detection using congruent deep learning models

Solar flares are energetic events in the solar atmosphere that are often linked with solar radio bursts (SRBs). SRBs are observed at metric to decametric wavelengths and are classified into five spectral classes (Type I–V) based on their signature in dynamic spectra. The automatic detection and classification of SRBs is a challenge due to their heterogeneous form. Near-real time detection and classification of SRBs has become a necessity in recent years due to large data rates generated by advanced radio telescopes such as the LOw Frequency ARray (LOFAR). For this study, we implemented congruent deep learning models to automatically detect and classify Type III SRBs. We generated simulated Type III SRBs, which were comparable to Type IIIs seen in real observations, using a deep learning method known as the generative adversarial network (GAN). This simulated data were combined with observations from LOFAR to produce a training set that was used to train an object detection model known as you only look once (YOLOv2). Using this congruent deep learning model system, we can accurately detect Type III SRBs at a mean Average Precision (mAP) value of 77.71%.

Global Trends and Performances of Acupuncture Therapy on Bell's Palsy from 2000 to 2023: A Bibliometric Analysis.

Recent studies have shown that acupuncture may have great potential in the treatment of Bell's palsy. However, the bibliometric analysis of this field has not been summarized properly. Thus, the purpose of this study is to analyze the hotspot of acupuncture for Bell's Palsy. The core collection database of Web of Science was searched for relevant publications from 2000 to 2023, and countries, institutions, authors, keywords, and literature were analyzed and visualized by bibliometric softwareCiteSpace 5.1.R6, Vosviewer, BICOMB, and gCLUTO to explore the scientific achievements, research collaboration networks, research hot spots, and research trends. 229 publications were included in this study. The most cited journal is Journal of Otolaryngology-Head & Neck Surgery; the most prolific country is China; the most prolific author is Li Ying, moreover, the collaboration among scholars is poor; Kyung Hee University is the most prolific institution studying acupuncture for Bell's Palsy. Reference burst detection indicates that traditional Chinese Medicine philosophy, the role of acupuncture in the prognosis of facial palsy, mechanism of acupuncture to improve facial nerve function, and the use of electroacupuncture are starting to become new research hotspots. The field of acupuncture for Bell's Palsy has developed rapidly in recent years, and new research trends are mainly: combination with traditional Chinese medicine, the role of acupuncture in the prognosis of facial palsy, mechanism of acupuncture to improve facial nerve function, and the use of electroacupuncture. However, research in this field is still dominated by case reports and clinical trials, and there is a lack of large-scale, multicenter clinical trials and animal experiments there are still many problems in institutional cooperation and experimental design, which requires relevant researchers to strengthen cooperation and improve experimental design.

Advances in gut microbiome in metabonomics perspective: based on bibliometrics methods and visualization analysis.

Gastrointestinal microbial metabolomics is closely related to the state of the organism and has significant interaction with the pathogenesis of many diseases. Based on the publications in Web of Science Core Collection(WoSCC) from 2004 to 2022, this study conducted a bibliometric analysis of this field, aiming to understand its development trend and frontier, and provide basic information and potential points for in-depth exploration of this field. All articles on gastrointestinal flora and metabolism published from 2004 to 2022 were collected and identified in WoCSS. CiteSpace v.6.1 and VOSviewer v.1.6.15.0 were used to calculate bibliometric indicators, including number of publications and citations, study categories, countries/institutions, authors/co-cited authors, journals/co-cited journals, co-cited references, and keywords. A map was drawn to visualize the data based on the analysis results for a more intuitive view. There were 3811 articles in WoSCC that met our criteria. Analysis results show that the number of publications and citations in this field are increasing year by year. China is the country with the highest number of publications and USA owns the highest total link strength and citations. Chinese Acad Sci rank first for the number of institutional publications and total link strength. Journal of Proteome Research has the most publications. Nicholson, Jeremy K. is one of the most important scholars in this field. The most cited reference is "Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease". Burst detection indicates that Urine, spectroscopy, metabonomic and gut microflora are long-standing hot topics in this field, while autism spectrum disorder and omics are likely to be at the forefront of research. The study of related metabolic small molecules and the application of gastrointestinal microbiome metabolomics in various diseases are currently emerging research directions and frontier in this field. This study is the first to make a bibliometric analysis of the studies related to gastrointestinal microbial metabolomics and reveal the development trends and current research hotspots in this field. This can contribute to the development of the field by providing relevant scholars with valuable and effective information about the current state of the field.

Bibliometric analysis of research topics on blood-brain barrier breakdown and cognitive function over the last two decades (2000-2021).

Blood-brain barrier (BBB) breakdown is closely associated with cognitive dysfunction. This study aimed to categorize and summarize research topics on the correlation between BBB breakdown and its effects on cognitive function. Bibliometric analysis methods were used to quantitatively and qualitatively assess research progress and predict future research hotspots. Relevant publications from the Web of the Science Core Collection were extracted on November 5, 2022 and analyzed to predict trends and hotspots in the field. We identified 5518 articles published from 2000 to 2021 about the BBB and cognition. The number of manuscripts on this topic increased steadily during this time period, especially after 2013. We found that the number of articles published in China increased gradually and is in second place behind the United States of America (USA). In the research field of BBB breakdown and cognitive function, the USA is still far ahead. Keyword burst detection suggested that cognitive impairment, neurodegeneration disease and neuroinflammation are emerging research hotspots. The mechanisms of BBB integrity breakdown and its effects on the deterioration of cognitive function are complex, and clinical treatment of the affected diseases has been a hot topic in the field over the past 22 years. Looking forward, this body of research is aimed at improving or maintaining patients' cognitive abilities, by finding preventive measures and to provide a basis for finding new treatments of cognitive disorders.

Application of Software and Hardware-Based Technologies in Leaks and Burst Detection in Water Pipe Networks: A Literature Review

With the rise of smart water cities, water resource management has become increasingly important. The increase in the use of intelligent leak detection technologies in the water, gas, oil, and chemical industries has led to a significant improvement in safety, customer, and environmental results, and management costs. The aim of this review article is to provide a comprehensive overview of the application of software and hardware-based technologies in leak detection and bursts in water pipeline networks. This review aims to investigate the existing literature on the subject and to analyse the key leak detection systems in the water industry. The novelty of this review is the comprehensive analysis of the literature on software and hardware-based technologies for leak and burst detection in water pipe networks. Overall, this review article contributes to understanding the latest developments and challenges in the application of software- and hardware-based technologies for leak and burst detection in water pipe networks, and serves as a valuable resource for researchers, engineers, and practitioners working in the field of water distribution systems.

Mapping current status and emerging trends in NETosis: A bibliometric study.

NETosis is a critical innate immune mechanism of neutrophils that contributes to the accelerated progression of autoimmune diseases, thrombosis, cancer, and coronavirus disease 2019 (COVID-19). This study qualitatively and quantitatively analyzed the relevant literature by bibliometric methods in order to provide a more comprehensive and objective view of the knowledge dynamics in the field. The literature on NETosis was downloaded from the Web of Science Core Collection, analyzed with VOSviewer, CiteSpace, and Microsoft for co-authorship, co-occurrence, and co-citation analysis. In the field of NETosis, the United States was the most influential countries. Harvard University was the most active institutions. Mariana J. Kaplan and Brinkmann V were, respectively, the most prolific and most co-cited authors. Frontiers in Immunology, Journal of Immunology, Plos One, Blood, Science, Journal of Cell Biology, and Nature Medicine were the most influential journals. The top 15 keywords are associated with immunological and NETosis formation mechanisms. The keywords with the strongest burst detection were mainly related to COVID-19 (coronavirus, ACE2, SARS coronavirus, cytokine storm, pneumonia, neutrophil to lymphocyte ratio), and cancer (circulating tumor cell). Research on NETosis is currently booming. The mechanism of NETosis and its role in innate immunity, autoimmune diseases, especially systemic lupus erythematosus and rheumatoid arthritis, and thrombosis are the focus of research in the field of NETosis. A future study will concentrate on the function of NETosis in COVID-19 and recurrent metastasis of cancer.

Real-time Spread Burst Detection in Data Streaming.

Data streaming has many applications in network monitoring, web services, e-commerce, stock trading, social networks, and distributed sensing. This paper introduces a new problem of real-time burst detection in flow spread, which differs from the traditional problem of burst detection in flow size. It is practically significant with potential applications in cybersecurity, network engineering, and trend identification on the Internet. It is a challenging problem because estimating flow spread requires us to remember all past data items and detecting bursts in real time requires us to minimize spread estimation overhead, which was not the priority in most prior work. This paper provides the first efficient, real-time solution for spread burst detection. It is designed based on a new real-time super spreader identifier, which outperforms the state of the art in terms of both accuracy and processing overhead. The super spreader identifier is in turn based on a new sketch design for real-time spread estimation, which outperforms the best existing sketches.

Bibliometric and visualized analysis of applying tumor markers in lung cancer diagnosis from 2000 to 2022.

Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. Tumor marker (TM) detection can indicate the existence and growth of a tumor and has therefore been used extensively for diagnosing LC. Here, we conducted a bibliometric analysis to examine TM-related publications for LC diagnosis to illustrate the current state and future trends of this field, as well as to identify additional promising TMs with high sensitivity. Publications regarding TMs in LC diagnosis were downloaded from the Web of Science Core Collection. CiteSpace was applied to perform a bibliometric analysis of journals, cocitation authors, keywords, and references related to this field. VOSviewer was used to generate concise diagrams about countries, institutions, authors, and keywords. Changes in the TM research frontier were analyzed through citation burst detection. A total of 990 studies were analyzed in this work. The collaboration network analysis revealed that the People's Republic of China, Yonsei University, and Molina R were the most productive country, institution, and scholar, respectively. Additionally, Molina R was the author with the most citations. The National Natural Science Foundation of China was the largest funding source. "Carcinoembryonic antigen (CEA) as tumor marker in lung cancer" was the top reference with the most citations, Lung Cancer was the core journal, and "serum tumor marker" experienced a citation burst over the past 5 years. This bibliometric analysis of TMs in LC diagnosis presents the current trends and frontiers in this field. We summarized the research status of this field and the methods to improve the diagnostic efficacy of traditional serum TMs, as well as provided new directions and ideas for improving the LC clinical detection rate. Priority should be given to the transformation of computer-assisted diagnostic technology for clinical applications. In addition, circulating tumor cells, exosomes, and microRNAs were the current most cutting-edge TMs.

Bibliometric and visualized analysis of drug resistance in ovarian cancer from 2013 to 2022.

As one of the cancers that seriously threatens women's health, ovarian cancer has a high morbidity and mortality rate. Surgery and chemotherapy are the basic treatment strategies for ovarian cancer, and chemotherapy resistance is a significant factor in affecting the prognosis, survival cycle, and recurrence of ovarian cancer. This article aims to analyze articles about ovarian cancer and drug resistance via bibliometric software, offering new ideas and directions for researchers in this field. Both Citespace and Vosviewer are bibliometric software on the Java platform. Articles were collected on ovarian cancer and drug resistance in the Web of Science Core Collection database from 2013 to 2022. The countries, institutions, journals, authors, keywords, and references were analyzed, and the development status of this field was indicated from multiple perspectives. Studies on ovarian cancer and drug resistance generally showed an increasing trend from 2013 to 2022. The People's Republic of China and Chinese institutions contributed more to this field. Gynecologic Oncology published the most articles, and the journal with the most citations was Cancer Research. Li Li was the author with the most publications, and Siegel RL was the author with the most citations. Through burst detection, it can be found that the research hotspots in this field mainly focused on the in-depth exploration of the drug resistance mechanism of ovarian cancer and the progress of PARP inhibitors and bevacizumab in the treatment of ovarian cancer. Many studies on the mechanism of drug resistance in ovarian cancer have been discovered; however, the deeper mechanism remains to be explored. Compared with traditional chemotherapy drugs, PARP inhibitors and bevacizumab have shown better efficacy, but PARP inhibitors have initially shown drug resistance. The future direction of this field should be to overcome the resistance of existing drugs and actively develop new ones.

Global trends and hotspots on childhood obstructive sleep apnea: a 10-year bibliometric analysis.

Obstructive sleep apnea (OSA) is children's most common sleep-related breathing disorder. It may develop a wide range of severe complications if not diagnosed promptly and treated effectively. However, Childhood OSA has not specifically been analyzed using a bibliometric approach. We respectively collected the research results of childhood OSA from 2013 to 2022 through Web of Science and PubMed. Vosviewer, CiteSpace, and bibliometric online analysis platforms were used for visualizing and analyzing the literature. The MeSH terms were bi-clustered using the Bibliographic Item co-occurrence Matrix Builder (BICOMB) and graph clustering toolkit (gCLUTO) to identify the hotspots. 4022 publications were finally identified on childhood OSA from 2013 to 2022. The United States has the largest number of publications (1902), accounting for 47.29%. University of Cincinnati is the most productive organization (196), followed by the University of Pennsylvania (151). The most prolific journal was the International Journal of Pediatric Otorhinolaryngology, with 311 documents published. In comparison, Pediatrics is the most cited journal (6936). Gozal D ranked highest among all authors in publication (192). Burst detection shows continuous positive airway pressure, Robin sequence, and nocturnal oximetry are recent keywords of great interest to researchers. Five hotspots were identified by co-word biclustering. Research over the past ten years has been fruitful, establishing the foundation for childhood OSA. Clusters (0-4) of high-frequency Major Mesh topics have attracted extensive attention. Evaluation and treatment methods of childhood OSA remain major focuses. We believe this article will provide other researchers with new directions and may contribute to a future breakthrough in this field.

States and hotspots in Helicobacter pylori research from 2002 to 2021: A bibliometric analysis.

Recently, numerous publications on Helicobacter pylori (H. pylori) have been published, but bibliometric analyses on this research field are scarce. To address this gap, we conducted a bibliometric analysis to provide a comprehensive overview and to explore the current research states and hotspots in this field. Publications on H. pylori from 2002 to 2021 were retrieved from the Web of Science Core Collection database (WoSCC). Trends in publications and citations were analyzed using Excel 2021. VOSviewer and Citespace were used to perform bibliometrics analysis. 36,266 publications on H. pylori were retrieved from the WoSCC database. In general, we observed an increasing trend in the number of publications over the past 20 years. The United States was the most productive and influential country, with the largest proportion of both publications and total citations. Helicobacter, US Department of Veterans Affairs, and Graham, David were the most productive journals, institutions and authors, respectively. Further analysis the co-occurrence and burst detection of keywords revealed that the most common keywords were "Helicobacter pylori," "gastric cancer," and "gastritis," all keywords were divided into eight main clusters, and the most important current research hotspot was the relationship between H. pylori infection and the changes of gut microbiota. The United States has been the most productive and influential country on H. pylori research, and H. pylori-related research remains an active research field. The relationship between H. pylori infection and the changes of gut microbiota is a research hotspot attracting significant attention.

Approach for Near-Real-Time Pipe Burst Detection and Location Estimation Utilizing Any Sequence of Harmonics of the Transient Pressure Signal

The purpose of the paper is to present an approach for real-time pipe burst detection and location estimation, based on the changes in the harmonics and analysis of the corresponding damping that is caused by the burst. The amplitude of each resonance response of the transient pressure wave caused by the burst is damped differently due to the occurrence of the burst. However, utilizing higher order harmonics beyond the first few fundamental frequencies has not previously been possible as it leads to an increasing number of possible solutions to the burst location, which limits the application of the method. An algorithm for damping analysis has been developed which overcomes this limitation, and is able to exclude most of the incorrect solutions when utilizing the information contained in higher order harmonics. Additionally, the gap between data windows can be set so that it enables the real-time data analysis by letting the window gap equal the reciprocal of the sampling rate. Therefore, this algorithm makes utilizing any sequence of harmonics of the signal be possible for real-time burst detection and location estimation. The approach has been verified both numerically and experimentally with acceptable accuracy.

WALLABY Pilot Survey: H i in the Host Galaxy of a Fast Radio Burst

We report on the commensal ASKAP detection of a fast radio burst (FRB), FRB 20211127I, and the detection of neutral hydrogen (H i) emission in the FRB host galaxy, WALLABY J131913–185018 (hereafter W13–18). This collaboration between the CRAFT and WALLABY survey teams marks the fifth, and most distant, FRB host galaxy detected in H i, not including the Milky Way. We find that W13–18 has an H i mass of M HI = 6.5 × 109 M ⊙, an H i-to-stellar mass ratio of 2.17, and coincides with a continuum radio source of flux density at 1.4 GHz of 1.3 mJy. The H i global spectrum of W13–18 appears to be asymmetric, albeit the H i observation has a low signal-to-noise ratio (S/N), and the galaxy itself appears modestly undisturbed. These properties are compared to the early literature of H i emission detected in other FRB hosts to date, where either the H i global spectra were strongly asymmetric, or there were clearly disrupted H i intensity map distributions. W13–18 lacks a sufficient S/N to determine whether it is significantly less asymmetric in its H i distribution than previous examples of FRB host galaxies. However, there are no strong signs of a major interaction in the optical image of the host galaxy that would stimulate a burst of star formation and hence the production of putative FRB progenitors related to massive stars and their compact remnants.

Bibliometric analysis of Helicobacter pylori resistance-from 2002 to 2022.

Drug resistance in Helicobacter pylori severely affects the efficacy of eradication therapy, and a number of studies have been conducted on this issue. The aim of this study was to assess the progress in this field using a bibliometric approach. Publications related to H. pylori resistance from 2002 to 2022 were retrieved from the Web of Science database. Relevant information including titles, authors, countries, and keywords was extracted, and the data were processed using Excel, VOSviewer, and CiteSpace software for co-authorship, co-citation, and co-occurrence analysis. From 2002 to 2022 (as of 09/24/2022), the field of H. pylori-resistance research produced a total of 2677 publications with a total of 75217 citations, with an overall upward trend in the annual number of articles published, reaching a peak of 204 in 2019. Articles were mainly published in Q1 or Q2 journals, with Helicobacter (TP = 261) publishing the most literature, Baylor College of Medicine (TP = 68) and Deng-chyang wu (TP = 38) being the most prolific institutions and authors, respectively. China and the United States were the locations of most of the articles, accounting for 35.08% of the global publication volume. Keyword co-occurrence analysis divided H. pylori-resistance research into four clusters: "Therapeutic Strategies," "Diseases," "Mechanism Research and Epidemiology," and "Drug Research." "Drug research" and burst detection indicate that the current research hotspot involves the selection and analysis of treatment strategies. H. pylori-resistance research has become a popular research field, and although there are significant contributions from Europe, the United States, and East Asia, there are significant imbalances between regions that cannot be ignored. In addition, the exploration of treatment strategies remains a key issue for research at the current stage.

The Evolution of FinTech in Scientific Research: A Bibliometric Analysis

This study aims to investigate how FinTech evolved over time in research using bibliometric analysis. First, 1359 publications particularly related to the field of FinTech, from 2010 to 2021, were collected from the Scopus database. This study analyzed the fundamental issues and characteristics of FinTech in research, such as the annual contribution of publication, hot in the press, and foci, using theme analysis, concurrence analysis, and timeline analysis of authors’ keywords. Second, prolific objects, such as journals, authors, institutions, countries, regions, and corresponding pivotal cooperative relationship mapping were used to present who leads considerable attention in FinTech research. Third, the citation structures of authors and journals were investigated, and presented the burst detection analysis of cited authors, journals, and references. Finally, combining the analysis results with the current financial environment, the challenges and future development opportunities are discussed further. This bibliometric analysis revealed an increasing annual publication trend, a shifting focus on financial inclusion, a dominance of authors from the USA, and a growing number of international collaborations and publications from different sources, indicating FinTech as a lively field with potential for further scientific enrichment. Accordingly, this comprehensive study of the FinTech documents not only reviews the current research characteristics and trajectories, but also helps scholars find the appropriate research entry-point and conduct in-depth research.

Pipeline-Burst Detection on Imbalanced Data for Water Supply Networks

Data-driven methods based on samples from a supervisory control and data acquisition system have been widely applied in water-supply-network burst detection to save unexpected economic and labor costs. However, the class imbalance problem in actual on-site monitoring needs to be revised to improve the performance of data-driven methods. In this study, we proposed a domain adaptation method to generate minor-category samples (pipeline-burst samples in general) of arbitrary pipe networks utilizing theoretical hydraulic models. The proposed method transferred pipeline-burst data generated from a random water supply network with theoretical hydraulic models to an actual imbalanced dataset. Accordingly, we established a machine learning model exploring a mapping matrix between two domains for minority-category data transfer. The experimental validation first verified the effectiveness and reliability of the proposed method between two customized water supply networks in terms of their bust recognition accuracy, model parameter sensitivity and time efficiency. Then, an actual monitoring dataset from a working water supply network was used to prove the suitability and compatibility of the proposed method. A bust-point location method was also provided based on the detection results of pipeline-bursting events. The validations show the superiority of our proposed approach for the imbalance data problem in pipe burst detection.

Approaches to characterizing oscillatory burst detection algorithms for electrophysiological recordings

BackgroundCognitive processes are associated with fast oscillations of the local field potential and electroencephalogram. There is a growing interest in targeting them because these are disrupted by aging and disease. This has proven challenging because they often occur as short-lasting bursts. Moreover, they are obscured by broad-band aperiodic activity reflecting other neural processes. These attributes have made it exceedingly difficult to develop analytical tools for estimating the reliability of detection methods. New methodTo address this challenge, we developed an open-source toolkit with four processing steps, that can be tailored to specific brain states and individuals. First, the power spectrum is decomposed into periodic and aperiodic components, each of whose properties are estimated. Second, the properties of the transient oscillatory bursts that contribute to the periodic component are derived and optimized to account for contamination from the aperiodic component. Third, using the burst properties and aperiodic power spectrum, surrogate neural signals are synthesized that match the observed signal’s spectrotemporal properties. Lastly, oscillatory burst detection algorithms run on the surrogate signals are subjected to a receiver operating characteristic analysis, providing insight into their performance. ResultsThe characterization algorithm extracted features of oscillatory bursts across multiple frequency bands and brain regions, allowing for recording-specific evaluation of detection performance. For our dataset, the optimal detection threshold for gamma bursts was found to be lower than the one commonly used. Comparison with existing methodsExisting methods characterize the power spectrum, while ours evaluates the detection of oscillatory bursts. ConclusionsThis pipeline facilitates the evaluation of thresholds for detection algorithms from individual recordings.

Information and Communication Technology Applications in Construction Organizations: a Scientometric Review

The application and research of Information and Communication Technology has exponentially grown in construction and is of a multidiscipline nature. However, there is a lack of investigation into progression of ICT research topics within the construction disciplines and the knowledge domain has not been deeply explored. This research fills this gap using a scientometric review of construction ICT research. Relevant literature from reputable sources are reviewed and classified to create a comprehensive framework for ICT application in construction. Accordingly, a manual review of research titles, as well as abstracts, was conducted. Accordingly, five analysis steps were performed including “Journals Selection, Publication Type and Name” “Citation Analysis”, “Keywords Co-Occurrence Network”, “Cluster Analysis”, and “Keyword time analysis”. A total of 376 research selected for burst detection, co-citation analysis, cluster analysis, keyword co-releases, and ICT application analysis in order to provide a comprehensive knowledge summary of the ICT application in the construction industry. Automation in Construction and Journal of Management in Engineering were identified as the major journals associated with research on the ICT application in the construction industry. Based on the research method, the articles used for scientometric analysis were only selected from Scopus and Web of Science database. Moreover, the study mainly concentrated on the quantitative analysis in regard to articles emerging frequently the knowledge domain, and were used to indicate the trends, timelines and hot topics. This research explores the construction industry body of knowledge by classifying the existing ICT related research, evolutionary trend, providing current status, a comprehensive knowledge framework, and future directions.

Research trends and prospects on brain metastasis from breast cancer: A bibliometric analysis.

Brain metastasis is the terminal event of breast cancer with poor prognoses. Therefore, this article aimed to provide an updated summary on the development, hotspots, and research trends of brain metastasis from breast cancer based on bibliometric analysis. Publications on breast cancer with brain metastasis retrieved from the Web of Science Core Collection. CiteSpace, VOSviewer, and other online bibliometric analysis platforms were used to analyze and visualize the result. In totality, 693 researchers from 3,623 institutions across 74 counties and regions published a total of 2,790 papers in 607 journals. There was a noticeable increase in publications in 2006. The United States was the dominant country with the most publications followed by China. University Texas MD Anderson Cancer Center was the most productive institution, while Dana Farber Cancer Institution was the most cited. Journal of Neuro-Oncology published the most papers, while Journal of Clinical Oncology ranked first based on cocited analysis. Nancy U. Lin was the most productive and cited author with high influence. There was a focus on basic research, clinical trials, local therapy, treatment optimization, and epidemiological studies regarding brain metastases from breast cancer. References focused on pathogenesis, prevention, treatment, and prognosis were cited most frequently, among which the clinical trial of novel treatment attracted most attention from researchers. Reference citation burst detection suggested that new therapies such as the novel tyrosine kinase inhibitor and antibody-drug conjugate may lead the research trends in the future. High-income countries contributed more to the field of breast cancer with brain metastasis, while developing countries like China developed quickly. Furthermore, the success of novel therapies in recent years may lead to the new era of treatment of breast cancer with brain metastasis in the future.

Coherent radio bursts from known M-dwarf planet-host YZ Ceti

Observing magnetic star–planet interactions (SPIs) offers promise for determining the magnetic fields of exoplanets. Models of sub-Alfvénic SPIs predict that terrestrial planets in close-in orbits around M dwarfs can induce detectable stellar radio emission, manifesting as bursts of strongly polarized coherent radiation observable at specific planet orbital positions. Here we present 2–4 GHz detections of coherent radio bursts on the slowly rotating M dwarf YZ Ceti, which hosts a compact system of terrestrial planets, the innermost of which orbits with a two-day period. Two coherent bursts occur at similar orbital phases of YZ Ceti b, suggestive of an enhanced probability of bursts near that orbital phase. We model the system’s magnetospheric environment in the context of sub-Alfvénic SPIs and determine that YZ Ceti b can plausibly power the observed flux densities of the radio detections. However, we cannot rule out stellar magnetic activity without a well-characterized rate of non-planet-induced coherent radio bursts on slow rotators. YZ Ceti is therefore a candidate radio SPI system, with unique promise as a target for long-term monitoring.