live long and prosper

Houston scores respectable rank in new report of best U.S. cities for working-class families

Houstoians live well.

A new economic analysis by the Ludwig Institute for Shared Economic Prosperity (LISEP) has revealed that Houston-The-Woodlands-Sugar Land has the 30th most prosperous local economy in the nation. The analysis ranked the 50 largest Metropolitan Statistical Areas (MSAs) in the U.S. based on several localized economic factors.

According to Houston's data, the region's population has skyrocketed 18.7 percent within the last decade, now reaching over 7.34 million people. The median age of an Houston-area resident is 35.1-years-old.

The three main criteria that determined Houston's prospering rank include a "True Living Cost" metric that tracks price changes for essential household necessities; a "True Weekly Earnings" calculation that determines the median weekly earnings of all workers (including part-timers and those who are unemployed); and a "True Rate of Unemployment Out of the Population" metric that measures the percentage of people unable to find full-time employment with a living wage.

The findings show that a little more than half of all Houston-area households are earning enough income to afford their basic needs. The remaining 46.4 percent are struggling due to a high cost of living, the report said.

"The total costs of necessities for a 4-person family [in Houston-The Woodlands-Sugar Land] increased 58.2 percent since 2005 from $54,052 to $85,492," the report said.

Furthermore, Houstonians are bringing home higher weekly earnings than they were in 2020. Houston workers are earning a median $894.89 per week, or about $47,429.17 a year. The report states the average employee has gained 8.2 percent more purchasing power since 2005, and the average Houston household has 8.1 percent of their income leftover after their necessities.

Houston's "True Rate of Unemployment Out of the Population" is 63 percent, according to the analysis.

The objective behind LISEP's report is to help policymakers assess their local economies' dynamics and to assess how much low-income and working-class families are affected, according to LISEP Chairman Gene Ludwig in the release

"Across the nation we are seeing both ends of the spectrum — communities where middle- and working-class families are faring well and others where financial survival remains a struggle," Ludwig said. "Our challenge here is in identifying what's working well and replicating it; what's not, and scrapping it. This is where real-world data can be invaluable to policymakers."

Houston-The Woodlands-Sugar Land wasn't the only Texas metro area to earn a spot in the analysis. Austin-Round Rock worked its way up into No. 2 nationally, while San Antonio-New Braunfels ranked just outside the top 10 at No. 13. Dallas-Fort Worth-Arlington ranked four spots higher than Houston at No. 26.

The top 10 highest-performing economies in the U.S. are:

No. 1 – San Jose-Sunnyvale, Santa Clara, California
No. 2 – Austin-Round Rock, Texas
No. 3 – San Francisco-Oakland-Hayward, California
No. 4 – Baltimroe-Columbia-Towson, Maryland
No. 5 – Washington-Arlington-Alexandria, District of Columbia-Virginia-Maryland
No. 6 – Minneapolis-St. Paul-Blookington, Minnesota-Wisconsin
No. 7 – Portland-Vancouver-Hillsboro, Oregon-Washington
No. 8 – Milwaukee-Waukesha-West Allis, Wisconsin
No. 9 – Denver-Aurora-Lakewood, Colorado
No. 10 – Salt Lake City, Utah

The full report and its methodology can be found on lisep.org.

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brain scientists at work

Rice University scientists invent new algorithm to fight Alzheimer's

Vicky Yao and Qiliang Lai of Rice University work on a laptop.

Photo courtesy of Rice University

Vicky Yao, an assistant professor of computer science and member of the Ken Kennedy Institute at Rice University, and Qiliang Lai, a Rice postdoctoral researcher

A new breakthrough from researchers at Rice University could unlock the genetic components that determine several human diseases such as Parkinson's and Alzheimer's.

Alzheimer's disease affected 57 million people worldwide in 2021, and cases in the United States are expected to double in the next couple of decades. Despite its prevalence and widespread attention of the condition, the full mechanisms are still poorly understood. One hurdle has been identifying which brain cells are linked to the disease.

For years, it was thought that the cells most linked with Alzheimer's pathology via DNA evidence were microglia, infection-fighting cells in the brain. However, this did not match with actual studies of Alzheimer's patients' brains. It's the memory-making cells in the human brain that are implicated in the pathology.

To prove this link, researchers at Rice alongside Boston University developed a computational algorithm called “Single-cell Expression Integration System for Mapping genetically implicated Cell types," or SEISMIC. It allows researchers to zero in on specific neurons linked to Alzheimer's, the first of its kind. Qiliang Lai, a Rice doctoral student and the lead author of a paper on the discovery published in Nature Communications, believes that this is an important step in the fight against Alzheimer's.

“As we age, some brain cells naturally slow down, but in dementia ⎯ a memory-loss disease ⎯ specific brain cells actually die and can’t be replaced,” said Lai. “The fact that it is memory-making brain cells dying and not infection-fighting brain cells raises this confusing puzzle where DNA evidence and brain evidence don’t match up.”

Studying Alzheimer's has been hampered by the limitations of computational analysis. Genome-wide association studies (GWAS) and single-cell RNA sequencing (scRNA-seq) map small differences in the DNA of Alzheimer's patients. The genetic signal in these studies would often over-emphasize the presence of infection fighting cells, essentially making the activity of those cells too "loud" statistically to identify other factors. Combined with greater specificity in brain regional activity, SEISMIC reduces the data chatter to grant a clearer picture of the genetic component of Alzheimer's.

“We built our seismic algorithm to analyze genetic information and match it precisely to specific types of brain cells,” Lai said. “This enables us to create a more detailed picture of which cell types are affected by which genetic programs.”

Though the algorithm is not in and of itself likely to lead to a cure or treatment for Alzheimer's any time soon, the researchers say that SEISMIC is already performing significantly better than existing tools at identifying important disease-relevant cellular signals more clearly.

“We think this work could help reconcile some contradicting patterns in the data pertaining to Alzheimer’s research,” said Vicky Yao, assistant professor of computer science and a member of the Ken Kennedy Institute at Rice. “Beyond that, the method will likely be broadly valuable to help us better understand which cell types are relevant in different complex diseases.”

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