Analyzing Sentiments and Diffusion Characteristics of COVID-19 Vaccine Misinformation Topics in Social Media

This study presents a data analytics framework that aims to analyze topics and sentiments associated with COVID-19 vaccine misinformation in social media. A total of 40,359 tweets related to COVID-19 vaccination were collected between January 2021 and March 2021. Misinformation was detected using multiple predictive machine learning models. Latent Dirichlet Allocation (LDA) topic model was used to identify dominant topics in COVID-19 vaccine misinformation. Sentiment orientation of misinformation was analyzed using a lexicon-based approach. An independent-samples t-test was performed to compare the number of replies, retweets, and likes of misinformation with different sentiment orientations. Based on the data sample, the results show that COVID-19 vaccine misinformation included 21 major topics. Across all misinformation topics, the average number of replies, retweets, and likes of tweets with negative sentiment was 2.26, 2.68, and 3.29 times higher, respectively, than those with positive sentiment.

A Survey on Embedding Dynamic Graphs

Embedding static graphs in low-dimensional vector spaces plays a key role in network analytics and inference, supporting applications like node classification, link prediction, and graph visualization. However, many real-world networks present dynamic behavior, including topological evolution, feature evolution, and diffusion. Therefore, several methods for embedding dynamic graphs have been proposed to learn network representations over time, facing novel challenges, such as time-domain modeling, temporal features to be captured, and the temporal granularity to be embedded. In this survey, we overview dynamic graph embedding, discussing its fundamentals and the recent advances developed so far. We introduce the formal definition of dynamic graph embedding, focusing on the problem setting and introducing a novel taxonomy for dynamic graph embedding input and output. We further explore different dynamic behaviors that may be encompassed by embeddings, classifying by topological evolution, feature evolution, and processes on networks. Afterward, we describe existing techniques and propose a taxonomy for dynamic graph embedding techniques based on algorithmic approaches, from matrix and tensor factorization to deep learning, random walks, and temporal point processes. We also elucidate main applications, including dynamic link prediction, anomaly detection, and diffusion prediction, and we further state some promising research directions in the area.

Multi-round encryption for COVID-19 data using the DNA key

The need for a reliable and fast encryption algorithm to encrypt medical data for patients is an extremely important topic to be considered especially during pandemic times such as the pandemic COVID-19. This pandemic forced governments and healthcare institutions to monitor COVID-19 patients. All the patient's data or records are also shared among healthcare researchers to be used to help them find vaccines or cures for this pandemic. Therefore, protecting such data (images, text) or records face an everincreasing number of risks. In this paper, a novel multi-round encryption algorithm based on deoxyribonucleic acid (DNA) is proposed. The significance of the proposed algorithm comes from using a different random key to perform simple and fast encryption operations on multiple rounds to achieve a high level of confusion and diffusion effects in encrypted data. Experiments were conducted using a set of datasets of various types such as Excel sheets, images, and database tables. The experiments were conducted to test the performance and security level of the proposed encryption algorithm against well-known algorithms such as data encryption standard (DES) and advanced encryption standard (AES). The experiments show an outstanding performance regarding the encryption time, key size, information entropy, and the avalanche effects.

Diagnosis of ulnar nerve entrapment anterior to the medial epicondyle by ultrasound elastography and diffusion tensor imaging with fiber tractography: a case report

Ulnar/cubital tunnel syndrome is the second most common compressive neuropathy of the upper limb. Permanent location of the ulnar nerve anterior to the medial epicondyle is extremely rare, with only five cases reported in the literature. Using ultrasound elastography and diffusion tensor imaging with fiber tractography, we diagnosed a case in which ulnar nerve entrapment was associated with anterior nerve location. Surgical release confirmed the diagnosis and the patient was symptom free 3 months after surgery.

Influence mechanism of excipients on drug crystallization: experimental investigation and model analysis and prediction

The influence of temperature, stirring speed, and excipients on crystal growth kinetics of mesalazine and allopurinol was investigated through experiment and chemical potential gradient model. The results indicated that the Diffusion-Surface Reaction model (DSR (1,2)) showed good performance in modeling API crystal growth kinetics within the ARDs of 4%. Excipients played a crucial role in inhibiting crystal growth in all the systems. It can not only improve the API solubility, but also reduce the crystal growth rate. By comparing diffusion rate and surface-reaction rate constant within the DSR (1,2) model, it was found that the controlling step of mesalazine crystallization was surface-reaction. Allopurinol crystallization was dominated by both surface-reaction and diffusion. Meanwhile, the crystal growth kinetics of mesalazine and allopurinol were predicted successfully with the ARDs of 2.53% and 4.78%. This work provided a mechanistic understanding of polymer influence on the inhibition of API crystal growth.