The present study explored the relationship between desired purchasing behavior and individual differences using two nationally-representative, longitudinal samples of the U.S. population early in the COVID-19 pandemic. Past research has shown that individual differences provide information about how one might respond to threat. Therefore, we predicted changes in desired purchasing behavior across different sociodemographic variables that might reflect those differences. Specifically, we investigated hypotheses related to political orientation, age, sexual orientation, socioeconomic status, and whether or not the participant had children. We measured participants’ reported desired purchasing behavior across eleven categories of goods and investigated the connection between specific demographic variables and desired purchasing behavior. We found that conservatives desired to purchase more basic protection goods (guns/ammunition, cash, gas) and that older people desired to purchase more cleaning supplies and toiletries. These findings illustrate possible explanations for purchasing behavior during the COVID-19 pandemic and reveal directions for marketing designed to influence purchasing behavior.

Introduction. Human papillomavirus (HPV) is the most common sexually transmitted infections globally. HPV is responsible for several health concerns including genital warts, cancer of the cervix, vulva, penis, anus, and oropharynx. In China, HPV infection accounts for 69.1% of invasive cervical cancer. Currently, there is no treatment for HPV infection, but HPV vaccination has been proven to be effective against HPV-related diseases. Given the highest rate of contracting HPV and suboptimal vaccination rate in college students including international students in the U.S., it is important to investigate key factors associated with vaccine uptake among Chinese international students. Purpose. This study aimed to investigate knowledge and awareness of HPV and the vaccine, attitudes, and vaccination intention in this population. We conducted a cross-sectional online survey via REDCap. Methods. Participants who were (1) Chinese international student at Arizona State University; (2) 18 and older; (3) able to read, speak and write in Chinese or English were recruited from Arizona State University. Descriptive statistics (mean, standard deviation, frequency) and inferential statistics (Chi-square test, independent t-test) were conducted using SPSS 26.0. Results. One hundred and ten participants were included in this study (56.4% female, mean age = 24, SD = 3.7). Female students had significantly higher HPV vaccination rate than males (p = 0.000). The mean knowledge score was 8.09 (SD = 1.35); female students were more likely to receive HPV education than males (p = 0.001). The most common source of education was friends (50.7%). Three most common perceived risks were not being sexually active, being male, and not having any physical signs and symptoms. The three most common facilitators were infection prevention, access to vaccination, and ability to afford vaccination. The three most common barriers were the cost, safety, and efficacy of HPV vaccine. In conclusion, gender disparities exist among Chinese<br/>international students’ HPV vaccine uptake and HPV related education. Implication. Although Chinese international students possess moderate to high level of knowledge about HPV and HPV vaccines, they lack education from credible sources. Culturally and gender appropriate education is needed in order to address barriers of getting HPV vaccination.

This paper investigates the challenges associated with creating engaging virtual programming during the COVID-19 pandemic through the event Playfest from the ASU Art Museum. A survey was created and given to participants of the live Zoom event to understand which aspects were a success from the audience perspective. Staff members from different job ranks were interviewed about the internal structure in place for altering the popular in-person event into a digital one. Even though the COVID-19 pandemic will not last forever, exploring how to create virtual programming that is successful at engaging audiences allows for museums to remain relevant in a world where digital media is frequently consumed.

HackerHero is an educational game designed to teach children, especially those from marginalized backgrounds, computation thinking skills needed for STEAM fields. It also teaches children about social injustice. This project was focused on creating an audio visualization for an AI character within the HackerHero game. The audio visualization consisted of a static silhouette of a face and a wave-like form to represent the mouth. Audio content analysis was performed on audio sampled from the character’s voice lines. Pitch and amplitude derived from the analysis was used to animate the character’s visual features such as it’s brightness, color, and mouth movement. The mouth’s movement and color was manipulated with the audio’s pitch. The lights of Wave were controlled by the amplitude of the audio. Design considerations were made to accommodate those with visual disabilities such as color blindness and epilepsy. Overall the final audio visualization satisfied the project sponsor and built upon existing audio visualization work. User feedback will be a necessity for improving the audio visualization in the future.

The field of biomedical research relies on the knowledge of binding interactions between various proteins of interest to create novel molecular targets for therapeutic purposes. While many of these interactions remain a mystery, knowledge of these properties and interactions could have significant medical applications in terms of understanding cell signaling and immunological defenses. Furthermore, there is evidence that machine learning and peptide microarrays can be used to make reliable predictions of where proteins could interact with each other without the definitive knowledge of the interactions. In this case, a neural network was used to predict the unknown binding interactions of TNFR2 onto LT-ɑ and TRAF2, and PD-L1 onto CD80, based off of the binding data from a sampling of protein-peptide interactions on a microarray. The accuracy and reliability of these predictions would rely on future research to confirm the interactions of these proteins, but the knowledge from these methods and predictions could have a future impact with regards to rational and structure-based drug design.