Oropharyngeal cancer and human papillomavirus: a visualization based on bibliometric analysis and topic modeling

ObjectivesThe incidence of oropharyngeal cancer (OPC) is increasing. This study used bibliometric analysis and topic modeling to explore the research trends and advancements in this disease over the past 10 years, providing valuable insights to guide future investigations.Methods7,355 English articles from 2013 to 2022 were retrieved from the Web of Science Core Collection for bibliometric analysis. Topic modeling was applied to 1,681 articles from high-impact journals, followed by an assessment of topic significance ranking (TSR). Medical Subject Headings (MeSH) terms were extracted using R and Python, followed by an analysis of the terms associated with each topic and on an annual basis. Additionally, genes were extracted and the number of genes appearing each year and the newly emerged genes were counted.ResultsThe bibliometric analysis suggested that the United States and several European countries hold pivotal positions in research. Current research is focused on refining treatments, staging and stratification. Topic modeling revealed 12 topics, emphasizing human papillomavirus (HPV) and side effect reduction. MeSH analysis revealed a growing emphasis on prognosis and quality of life. No new MeSH terms emerged after 2018, suggesting that the existing terms have covered most of the core concepts within the field of oropharyngeal cancers. Gene analysis identified TP53 and EGFR as the most extensively studied genes, with no novel genes discovered after 2019. However, CD69 and CXCL9 emerged as new genes of interest in 2019, reflecting recent research trends and directions.ConclusionHPV-positive oropharyngeal cancer research, particularly treatment de-escalation, has gained significant attention. However, there are still challenges in diagnosis and treatment that need to be addressed. In the future, more research will focus on this issue, indicating that this field still holds potential as a research hotspot

Boosting microscopic object detection via feature activation map guided poisson blending

Microscopic examination of visible components based on micrographs is the gold standard for testing in biomedical research and clinical diagnosis. The application of object detection technology in bioimages not only improves the efficiency of the analyst but also provides decision support to ensure the objectivity and consistency of diagnosis. However, the lack of large annotated datasets is a significant impediment in rapidly deploying object detection models for microscopic formed elements detection. Standard augmentation methods used in object detection are not appropriate because they are prone to destroy the original micro-morphological information to produce counterintuitive micrographs, which is not conducive to build the trust of analysts in the intelligent system. Here, we propose a feature activation map-guided boosting mechanism dedicated to microscopic object detection to improve data efficiency. Our results show that the boosting mechanism provides solid gains in the object detection model deployed for microscopic formed elements detection. After image augmentation, the mean Average Precision (mAP) of baseline and strong baseline of the Chinese herbal medicine micrograph dataset are increased by 16.3% and 5.8% respectively. Similarly, on the urine sediment dataset, the boosting mechanism resulted in an improvement of 8.0% and 2.6% in mAP of the baseline and strong baseline maps respectively. Moreover, the method shows strong generalizability and can be easily integrated into any main-stream object detection model. The performance enhancement is interpretable, making it more suitable for microscopic biomedical applications

A dual functional peptide carrying in vitro selected catalytic and binding activities

When minimal functional sequences are used, it is possible to integrate multiple functions on a single peptide chain, like a “single stroke drawing”.</p

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

A Novel Method to Estimate the Sea State for Recycling Work on the Sea Surface

The recycling of marine exploration equipment after it has surfaced is greatly affected by sea state. In order to estimate the sea state in real time, this paper proposes a method for measuring wave elevation, which modifies the integrated results of GNSS/SINS in the up direction by virtual horizontal lines to extract wave fluctuation information. From these wave information, the significant wave heights (SWH) can be calculated as the only input parameter of P-M spectrum, and a series of wave height data can be further simulated. When the GNSS is interrupted due to severe sea state, the simulated data can be integrated with the SINS to deal with the data distortion problem. The simulation results show that the application of wave spectrum in the GNSS intermittent situation has obvious improvement effect and important significance

Collaborative Accurate Vehicle Positioning Based on Global Navigation Satellite System and Vehicle Network Communication

Intelligence is a direction of development for vehicles and transportation. Accurate vehicle positioning plays a vital role in intelligent driving and transportation. In the case of obstruction or too few satellites, the positioning capability of the Global navigation satellite system (GNSS) will be significantly reduced. To eliminate the effect of unlocalization due to missing GNSS signals, a collaborative multi-vehicle localization scheme based on GNSS and vehicle networks is proposed. The vehicle first estimates the location based on GNSS positioning information and then shares this information with the environmental vehicles through vehicle network communication. The vehicle further integrates the relative position of the ambient vehicle observed by the radar with the ambient vehicle position information obtained by communication. A smaller error estimate of the position of self-vehicle and environmental vehicles is obtained by correcting the positioning of self-vehicle and environmental vehicles. The proposed method is validated by simulating multi-vehicle motion scenarios in both lane change and straight-ahead scenarios. The root-mean-square error of the co-location method is below 0.5 m. The results demonstrate that the combined vehicle network communication approach has higher accuracy than single GNSS positioning in both scenarios