Mapping Intellectual Structures and Research Hotspots of Triple Negative Breast Cancer: A Bibliometric Analysis

BackgroundTriple negative breast cancer (TNBC) is a highly heterogeneous breast cancer subtype with a poor prognosis due to its extremely aggressive nature and lack of effective treatment options. This study aims to summarize the current hotspots of TNBC research and evaluate the TNBC research trends, both qualitatively and quantitatively.MethodsScientific publications of TNBC-related studies from January 1, 2010 to October 17, 2020 were obtained from the Web of Science database. The BICOMB software was used to obtain the high-frequency keywords layout. The gCLUTO was used to produce a biclustering analysis on the binary matrix of word-paper. The co-occurrence and collaboration analysis between authors, countries, institutions, and keywords were performed by VOSviewer software. Keyword burst detection was performed by CiteSpace.ResultsA total of 12,429 articles related to TNBC were identified. During 2010-2020, the most productive country/region and institution in TNBC field was the USA and The University of Texas MD Anderson Cancer Center, respectively. Cancer Research, Journal of Clinical Oncology, and Annals of Oncology were the first three periodicals with maximum publications in TNBC research. Eight research hotspots of TNBC were identified by co-word analysis. In the core hotspots, research on neoadjuvant chemotherapy, paclitaxel therapy, and molecular typing of TNBC is relatively mature. Research on immunotherapy and PARP inhibitor for TNBC is not yet mature but is the current focus of this field. Burst detection of keywords showed that studies on TNBC proteins and receptors, immunotherapy, target, and tumor cell migration showed bursts in recent three years.ConclusionThe current study revealed that TNBC studies are growing. Attention should be paid to the latest hotspots, such as immunotherapy, PARP inhibitors, target, and TNBC proteins and receptors.

Robust Burst Detection Algorithm for Distributed Unique Word TDMA Signal

In recent years, distributed unique word (DUW) has been widely used in satellite single carrier TDMA signals, such as very small aperture terminal (VSAT) satellite systems. Different from the centralized structure of traditional unique word, DUW is uniformly dispersed in a burst signal, where the traditional unique word detection methods are not applicable anymore. For this, we propose a robust burst detection algorithm based on DUW. Firstly, we allocated the sliding detection windows with the same structures as DUW in order to effectively detect it. Secondly, we adopt the method of time delay conjugate multiplication to eliminate the influence of frequency offset on detection performance. Due to the uniform dispersion of DUW, it naturally has two different kinds of time delays, namely the delay within the group and the delay between the two groups. So, we divide the traditional dual correlation formula into two parts to calculate them separately and obtain a dual correlation detection algorithm, which is suitable for DUW. Simulation and experimental results demonstrate that when the distribution structure of DUW changes, detection probability of the proposed algorithm fluctuates little, and its variance is 1.56×10−5, which is 99.83% lower than the existing DUW detection algorithms. In addition, its signal to noise ratio (SNR) threshold is about 1 dB lower than the existing algorithms under the same circumstance of the missed detection probability.

Real-time burst detection based on multiple features of pressure data

Pipe bursts are an essential issue for water loss in water distribution systems. This study proposes a real-time burst detection method that combines multiple data features of multiple time steps. The method sets burst thresholds in three dimensions according to different moments at a specific monitoring point, and achieves burst identification based on a classification model. First, three data features, namely, absolute pressure value, predicted deviation value obtained by prediction model, and pressure variation value, of historical pressure at each time step are scored based on the Western Electric Company rules. The scores represent different abnormalities. Then, the scores corresponding to the three features are used as input of the decision tree classification model. The trained model is used for detecting burst events. Results show that this method achieves 99.56% detection accuracy, indicating that it is effective for burst detection. The proposed method outperformed the single feature-based method and provides good results in water distribution systems.

Burst Detection-based Selective Classifier Resetting

Concept drift detection algorithms have historically been faithful to the aged architecture of forcefully resetting the base classifiers for each detected drift. This approach prevents underlying classifiers becoming outdated as the distribution of a data stream shifts from one concept to another. In situations where both concept drift and temporal dependence are present within a data stream, forced resetting can cause complications in classifier evaluation. Resetting the base classifier too frequently when temporal dependence is present can cause classifier performance to appear successful, when in fact this is misleading. In this research, a novel architectural method for determining base classifier resets, Burst Detection-based Selective Classifier Resetting (BD-SCR), is presented. BD-SCR statistically monitors changes in the temporal dependence of a data stream to determine if a base classifier should be reset for detected drifts. The experimental process compares the predictive performance of state-of-the-art drift detectors in comparison to the “No-Change” detector using BD-SCR to inform and control the resetting decision. Results show that BD-SCR effectively reduces the negative impact of temporal dependence during concept drift detection through a clear negation in the performance of the “No-Change” detector, but is capable of maintaining the predictive performance of state-of-the-art drift detection methods.