We present an improved model for the absorption of X-rays in the interstellar medium (ISM) intended for use with data from future X-ray missions with larger e †ective areas and increased energy resolution such as Chandra and the X-Ray Multiple Mirror mission, in the energy range eV. Compared with Z100 previous work, our formalism includes recent updates to the photoionization cross section and revised abundances of the interstellar medium, as well as a treatment of interstellar grains and the molecule. H 2 We review the theoretical and observational motivations behind these updates and provide a subroutine for the X-ray spectral analysis program XSPEC that incorporates our model.

Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter’s Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019–June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment.

On March 8, 2020, there was a 650% increase in Twitter retweets using the term “Chinese virus” and related terms. On March 9, there was an 800% increase in the use of these terms in conservative news media articles. Using data from non-Asian respondents of the Project Implicit “Asian Implicit Association Test” from 2007–2020 ( n = 339,063), we sought to ascertain if this change in media tone increased bias against Asian Americans. Local polynomial regression and interrupted time-series analyses revealed that Implicit Americanness Bias—or the subconscious belief that European American individuals are more “American” than Asian American individuals—declined steadily from 2007 through early 2020 but reversed trend and began to increase on March 8, following the increase in stigmatizing language in conservative media outlets. The trend reversal in bias was more pronounced among conservative individuals. This research provides evidence that the use of stigmatizing language increased subconscious beliefs that Asian Americans are “perpetual foreigners.” Given research that perpetual foreigner bias can beget discriminatory behavior and that experiencing discrimination is associated with adverse mental and physical health outcomes, this research sounds an alarm about the effects of stigmatizing media on the health and welfare of Asian Americans.

We have used x-ray diffraction with subnanosecond temporal resolution to measure the lattice parameters of orthogonal planes in shock compressed single crystals of silicon (Si) and copper (Cu). Despite uniaxial compression along the (400) direction of Si reducing the lattice spacing by nearly 11%, no observable changes occur in planes with normals orthogonal to the shock propagation direction. In contrast, shocked Cu shows prompt hydrostaticlike compression. These results are consistent with simple estimates of plastic strain rates based on dislocation velocity data.

The purpose of this research was to examine the psychometric properties of the Giscombe Superwoman Schema Questionnaire. Three separate studies conducted with 739 African American women provided preliminary evidence that the Questionnaire's factor structure aligns with the Superwoman Schema Conceptual Framework and has good reliability. In addition, it is positively associated with perceived stress, depressive symptoms, using food to cope with stress, poor sleep quality, and physical inactivity. This study provides preliminary evidence to suggest that the Giscombe Superwoman Schema Questionnaire is psychometrically sound; Superwoman Schema is associated with health behaviors and psychological states that may increase risk for illness.African American women experience disproportionately high rates of stress-related chronic health conditions compared to non-Hispanic white women. They are more likely to be overweight or obese and have higher rates of diabetes, cardiovascular disease, and morbidity related to a variety of other stress-related conditions (Centers for Disease Control and Prevention, 2013). African American women are also at higher risk for stress-related physiologic aging compared to white women (Geronimus, Hicken, Keene, & Bound, 2006;Geronimus et al., 2010), even after adjusting for socioeconomic factors.Over the past twenty years, mounting evidence has demonstrated links between psychological stress and adverse health outcomes among African American women (Allen