Muon tomography is one of the most important spinoffs of fundamental research with particle detectors -if not the most important. 

A sociological look at data from 2011-2018 led the authors of a new paper to cite an increase over time in psychological distress among Latinos, including citizens, in the U.S. They cite changes in the Deferred Action for Childhood Arrivals (DACA) program, which was deemed illegal by the courts, and President Biden threatening to shut the southern border of the U.S. entirely.

Any long-term effects of COVID-19, which originated in China and became the third coronavirus pandemic of the century, in the general adult population remain unclear. Some clearly have it while others are told it as an undefined blanket term, like fibromyalgia or chronic lyme disease.

A new paper claims that up to 10 percent of women who get COVID-19 during pregnancy will get a 'Long COVID' diagnosis. Their data are individuals from 46 states plus Washington, D.C. enrolled in the NIH RECOVER Initiative who got COVID-19 while pregnant and later got a Long COVID diagnosis.  

Cigarettes are a co-morbidity for almost everything and a risk factor for the rest, but it isn't just first-order disease that may be in the future of cigarette smokers.

A cohort of 1,000 healthy volunteers aged 20 to 70 in 2011 were examined to see why human immune systems vary significantly in terms of how effectively they respond to microbial attacks. Age, sex and genetics are known to have a significant impact on the immune system, the aim of this new study was to identify which other factors had the most influence.

If Google image search results overwhelmingly returned results showing men, that would be evidence that ending gender bias still has a long way to go. In the bias community, results showing women are the same thing.

And the authors of a new paper say female and male gender associations are more extreme among Google Images than within text from Google News; text is slightly more focused on men than women, this bias is over four times stronger in images.

Decades before solar and wind took over green marketing dollars, back when environmentalists still promoted natural gas and hydroelectric power, heat pumps became an energy-saving fad.

The problem with them became evident nearly as fast as that electric car range you think you'll get - it is only under ideal conditions in a lab. So if you bought one because you were told it is "400 percent efficient", you probably also bought organic food because someone told you it doesn't have pesticides. In other words, you were just believing in magic.

White people are more likely to confront those who post racist content on social media.

On surveys, at least, but on surveys very few people say they are anti-science, or even anti-vaccine. Not from 1998 to 2021, when coastal cities dominated vaccine exemptions, and not from 2021 on when middle states do. In both cases the argument is they support science but products need more testing, and they are anti-corporate.

New York City makes no sense on paper. It is expensive to get into, expensive to live in, yet crowded and dirty. The heat is overwhelming in the summer while in the winter the wind effect among all those buildings cut can through your parka.

There is no way to undo its monocentric development now, like California, New York is suffering a wealth and marriage diaspora for better tax and family environments, and “polycentric” spatial patterns may solve both those problems.

Transgender and gender-diverse Medicare beneficiaries use significantly more emergency department services than cisgender people, particularly for psychological care, and these visits were more likely to be followed by an admission.

It brings up an obvious question; with outsized use of emergency services, why are there delays in seeking timely health care that result in visits to the ER?

I recently held an accelerated course in "Statistical data analysis for fundamental science" for the Instats site. Within only 15 hours of online lectures (albeit these are full 1-hour blocks, unlike the leaky academic-style hours that last 75% of that) I had to cover not just parameter estimation, hypothesis testing, modeling, and goodness of fit, plus several ancillary concepts of high relevance such as ancillarity (yep), conditioning, the likelihood principle, coverage, and frequentist versus bayesian inference, but an introduction to machine learning! How did I do?