Saturday Citations: Bacterial warfare, a self-programming language model, passive cooling in the big city

There’s a lot of scientific news in seven days, so just because a new study isn’t cited here Saturday morning doesn’t mean it didn’t happen. a lot more has happened. But also check out these four stories:

Bacteria use phages as weapons

In agricultural environments on cultivated land, one variant of the bacterium Pseudomonas viridiflava will spread and become the dominant microbe, but on uncultivated land this does not happen. Researchers at the University of Utah wanted to find out why, but early in their research they discovered something so unexpectedly strange that it shifted the entire focus of the investigation.

While studying the genomes of bacterial pathogens, they discovered that one specimen had captured a phage – a virus that attacks bacteria – and reused it to kill its own bacterial competitors. Specifically, the bacteria acquired non-self-replicating clusters of the recycled phage called tailocins, which penetrate the outer membranes of pathogens and kill them.

Speculating on the finding, lead author Talia Backman says tailocins could potentially lead to new antibiotics to tackle the antimicrobial resistance crisis: “While tailocins have previously been found in other bacterial genomes and studied in laboratory settings, their impact and evolution in the wild is “That we found that these wild plant pathogens all have tailocins and that these tailocins evolve to kill neighboring bacteria shows how important they can be in nature.”

The language model programs itself to provide meaning

Large language models are capable of producing text. I was going to complement this thought with “that’s compellingly compelling” or something like that, but I had to stop and put my head on the table because the AI ​​hype cycle has broken its chain and become completely disconnected from reality and I’m tired.

Existing LLMs are nothing more than text predictors without any knowledge of semantics or logic, and therefore can only help people generate text that contains zero percent symbolic reasoning, and now they will be present on every device. Okay, I’m not here to complain about the AI ​​hype.

In an effort to improve the performance of LLMs, researchers at MIT have proposed an innovative technique to perform natural language, mathematics, and data analysis tasks by generating Python code. They call the approach embedded natural language programs and report 90% accuracy on a wide range of reasoning tasks.

The technique consists of four steps. In the first step, the NLEP calls the packages needed to perform a task. In the second step, it imports natural language representations of the knowledge or data that the task requires. In the third step, the model generates a function that calculates the answer. And in step four, the model generates the result in natural language. It is also more efficient for certain tasks, especially those where a user has many similar questions; instead of generating a new Python program for each query, the NLEP can generate one core program and change the variables for each query.

Shirt cool

As summer heat domes establish themselves over areas in the Northern Hemisphere, researchers from the UChicago Pritzker School of Molecular Engineering report a new wearable fabric that could protect city residents in the specific conditions of urban heat islands. Existing coolants work by diffusing direct sunlight. But sunlight is visible while the heat radiation from building materials, paving and infrastructure is infrared.

The engineers attempted to create a textile with two optical properties that could reflect both sources. And because it works passively, it can have cooling applications in areas with increasing energy consumption. In addition to heat-reflecting clothing, the material also has potential as a construction material to lower indoor temperatures, or as an insulator for cars. The researchers also suggest it could be used to transport perishable food, reducing the demand for active cooling systems.

Children are immature, research shows

Researchers from the National University of Singapore report that a lower ratio of neural excitation (E) to neural inhibition (I) is a positive sign of brain maturation. They find that children with a lower E/I ratio perform better at school and on cognitive tests. . Previous studies have shown that too much arousal or inhibition carries a higher risk of brain disorders, including autism, Alzheimer’s disease and others. The study examined how E/I changes in young people by analyzing MRI brain scans and cognitive test scores from 885 children, adolescents and young adults.

In particular, the team has developed a non-invasive technique for studying neural excitation and inhibition responses. In the first part of the experiment, subjects took the anti-anxiety drug Xanax or a placebo before the MRI brain scans. This allowed the researchers to determine that Xanax increases neural inhibition so that the overall E/I ratio decreases. In the second part of the experiment, the team established the link between the E/I ratio and cognitive function by administering cognitive tests. Participants with lower E/I performed better than those with higher ratios.