Breast Cancer Induced Bone Osteolysis Prediction Using Temporal Variational Autoencoders

Objective and Impact Statement.We adopt a deep learning model for bone osteolysis prediction on computed tomography(CT)images of murine breast cancer bone metastases.Given the bone CT scans at previous time steps,the model incorporates the bone-cancer interactions learned from the sequential images and generates future CT images.Its ability of predicting the development of bone lesions in cancer-invading bones can assist in assessing the risk of impending fractures and choosing proper treatments in breast cancer bone metastasis.Introduction.Breast cancer often metastasizes to bone,causes osteolytic lesions,and results in skeletal-related events(SREs)including severe pain and even fatal fractures.Although current imaging techniques can detect macroscopic bone lesions,predicting the occurrence and progression of bone lesions remains a challenge.Methods.We adopt a temporal variational autoencoder(T-VAE)model that utilizes a combination of variational autoencoders and long short-term memory networks to predict bone lesion emergence on our micro-CT dataset containing sequential images of murine tibiae.Given the CT scans of murine tibiae at early weeks,our model can learn the distribution of their future states from data.Results.We test our model against other deep learning-based prediction models on the bone lesion progression prediction task.Our model produces much more accurate predictions than existing models under various evaluation metrics.Conclusion.We develop a deep learning framework that can accurately predict and visualize the progression of osteolytic bone lesions.It will assist in planning and evaluating treatment strategies to prevent SREs in breast cancer patients.

Recent advances in artificial intelligence generated content

Artificial intelligence generated content(AIGC)has been a research hotspot in the field of artificial intelligence in recent years.It is expected to replace humans in performing some of the work of content generation at a low cost and a high volume,such as music,painting,multimodal content generation,news articles,summary reports,stock commentary summaries,and even content and digital people generated in the meta-universe.AIGC provides a new technical path for the development and implementation of AI in the future.

Characterizing Discourse about COVID-19 Vaccines:A Reddit Version of the Pandemic Story

It has been one year since the outbreak of the COVID-19 pandemic.The good news is that vaccines developed by several manufacturers are being actively distributed worldwide.However,as more and more vaccines become available to the public,various concerns related to vaccines become the primary barriers that may hinder the public from getting vaccinated.Considering the complexities of these concerns and their potential hazards,this study is aimed at offering a clear understanding about different population groups’underlying concerns when they talk about COVID-19 vaccines—particularly those active on Reddit.The goal is achieved by applying LDA and LIWC to characterize the pertaining discourse with insights generated through a combination of quantitative and qualitative comparisons.Findings include the following:(1)during the pandemic,the proportion of Reddit comments predominated by conspiracy theories outweighed that of any other topics;(2)each subreddit has its own user bases,so information posted in one subreddit may not reach that from other subreddits;and(3)since users’concerns vary across time and subreddits,communication strategies must be adjusted according to specific needs.The results of this study manifest challenges as well as opportunities in the process of designing effective communication and immunization programs.

Social media study of public opinions on potential COVID-19 vaccines: informing dissent, disparities, and dissemination

Background The current development of vaccines for severe acute respiratory syndrome coronavirus 2(SARSCoV-2)is unprecedented.Little is known,however,about the nuanced public opinions on the vaccines on social media.Methods We adopted a human-guided machine learning framework using more than six million tweets from almost two million unique Twitter users to capture public opinions on the vaccines for SARS-CoV-2,classifying them into three groups:pro-vaccine,vaccine-hesitant,and anti-vaccine.After feature inference and opinion mining,10,945 unique Twitter users were included in the study population.Multinomial logistic regression and counterfactual analysis were conducted.Results Socioeconomically disadvantaged groups were more likely to hold polarized opinions on coronavirus disease 2019(COVID-19)vaccines,either pro-vaccine(B=0.40,SE=0.08,P<0.001,OR=1.49;95%CI=1.26-1.75)or anti-vaccine(B=0.52,SE=0.06,P<0.001,OR=1.69;95%CI=1.49-1.91).People who have the worst personal pandemic experience were more likely to hold the anti-vaccine opinion(B=−0.18,SE=0.04,P<0.001,OR=0.84;95%CI=0.77-0.90).The United States public is most concerned about the safety,effectiveness,and political issues regarding vaccines for COVID-19,and improving personal pandemic experience increases the vaccine acceptance level.

Misinformation versus Facts:Understanding the Influence of News regarding COVID-19 Vaccines on Vaccine Uptake

Background. There is a lot of fact-based information and misinformation in the online discourses and discussions about theCOVID-19 vaccines. Method. Using a sample of nearly four million geotagged English tweets and the data from the CDCCOVID Data Tracker, we conducted the Fama-MacBeth regression with the Newey-West adjustment to understand theinfluence of both misinformation and fact-based news on Twitter on the COVID-19 vaccine uptake in the US from April 19when US adults were vaccine eligible to June 30, 2021, after controlling state-level factors such as demographics, education,and the pandemic severity. We identified the tweets related to either misinformation or fact-based news by analyzing theURLs. Results. One percent increase in fact-related Twitter users is associated with an approximately 0.87 decrease (B = −0:87,SE = 0:25, and p < :001) in the number of daily new vaccinated people per hundred. No significant relationship was foundbetween the percentage of fake-news-related users and the vaccination rate. Conclusion. The negative association between thepercentage of fact-related users and the vaccination rate might be due to a combination of a larger user-level influence and thenegative impact of online social endorsement on vaccination intent.

What Contributes to a Crowdfunding Campaign’s Success?Evidence and Analyses from GoFundMe Data

Researchers have attempted to measure the success of crowdfunding campaigns using a variety of determinants,such as the descriptions of the crowdfunding campaigns,the amount of funding goals,and crowdfunding project characteristics.Although many successful determinants have been reported in the literature,it remains unclear whether the cover photo and the text in the title and description could be combined in a fusion classifier to better predict the crowdfunding campaign’s success.In this work,we focus on the performance of the crowdfunding campaigns on GoFundMe across a wide variety of funding categories.We analyze the attributes available at the launch of the campaign and identify attributes that are important for each category of the campaigns.Furthermore,we develop a fusion classifier based on the random forest that significantly improves the prediction result,thus suggesting effective ways to make a campaign successful.