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Less is more: Gene loss drives adaptive evolution of a pandemic bacterium

A study published in Nature Ecology & Evolution reveals a surprising evolutionary insight: sometimes, losing genes rather than gaining them can help bacterial pathogens survive and thrive.

The study was conducted by a group of scientists and coordinated by Jaime Martínez Urtaza, from the Department of Genetics and Microbiology of the Universitat Autònoma de Barcelona (UAB); Yang Chao and Falush Daniel, from the Shanghai Institute of Immunity and Infection, Chinese Academy of Science; and Wang Hui, from the Shanghai Jiao Tong University.

When we think of evolution, we often imagine organisms changing or gaining to adapt, such as growing wings, developing resistance, or evolving new behaviors. Across the tree of life, both spontaneous mutations and gene acquisition are classic tools of adaptation. However, in this study, researchers went down a lesser known and scarcely explored evolutionary path, the one of gene loss.

AI and lab tests to predict genetic disease risk

When genetic testing reveals a rare DNA mutation, doctors and patients are frequently left in the dark about what it actually means. Now, researchers have developed a powerful new way to determine whether a patient with a mutation is likely to actually develop disease, a concept known in genetics as penetrance.

The team set out to solve this problem using artificial intelligence (AI) and routine lab tests like cholesterol, blood counts, and kidney function. Details of the findings were reported in the journal Science. Their new method combines machine learning with electronic health records to offer a more accurate, data-driven view of genetic risk.

Traditional genetic studies often rely on a simple yes/no diagnosis to classify patients. But many diseases, like high blood pressure, diabetes, or cancer, don’t fit neatly into binary categories. The researchers trained AI models to quantify disease on a spectrum, offering more nuanced insight into how disease risk plays out in real life.

Using more than 1 million electronic health records, the researchers built AI models for 10 common diseases. They then applied these models to people known to have rare genetic variants, generating a score between 0 and 1 that reflects the likelihood of developing the disease.

A higher score, closer to 1, suggests a variant may be more likely to contribute to disease, while a lower score indicates minimal or no risk. The team calculated “ML penetrance” scores for more than 1,600 genetic variants.

Some of the results were surprising, say the investigators. Variants previously labeled as “uncertain” showed clear disease signals, while others thought to cause disease had little effect in real-world data.

Integrated lithium niobate photonic computing circuit based on efficient and high-speed electro-optic conversion

Efficient electro-optic conversion is central to photonic computing, and thin-film lithium niobate (TFLN) offers this capability. Here, the authors demonstrate computing circuits on the TFLN platform, enabling the next generation of photonic computing systems featuring both high-speed and low-power.

CERN Deploys Cutting-Edge AI in “Impossible” Hunt for Higgs Decay

CMS employed machine learning to probe rare Higgs decays into charm quarks. The search produced the most stringent limits so far. The Higgs boson, first observed at the Large Hadron Collider (LHC) in 2012, is a cornerstone of the Standard Model of particle physics. Through its interactions, it

Rewriting Chemical Rules: Researchers Accidentally Create Unprecedented New Gold Compound

SLAC scientists created gold hydride in extreme lab conditions. The work sheds light on dense hydrogen and fusion processes. By chance and for the first time, an international team of researchers led by scientists at the U.S. Department of Energy’s SLAC National Accelerator Laboratory succeeded i

NASA to launch IMAP, Carruthers, and SWFO with support from Astrotech’s commercial facility

NASA is gearing up for a landmark late-September launch featuring three pivotal spacecraft: the Interstellar Mapping and Acceleration Probe (IMAP), the Carruthers Geocorona Observatory, and NOAA’s Space Weather Follow-On (SWFO-L1). The missions are being prepared at Astrotech Space Operations, a Lockheed Martin subsidiary in Titusville that has become one of the nation’s premier spacecraft processing hubs.

Astrotech regularly integrates spacecraft for NASA, the Department of Defense, and commercial providers, and recently hosted media for a rare look inside its cleanroom facilities.

Under the leadership of Principal Investigator David McComas, professor of astrophysical sciences at Princeton University, and built by Johns Hopkins University Applied Physics Laboratory, IMAP continues the legacy of NASA’s 2008 IBEX mission.

Association between Coffee Consumption and Brain MRI Parameters in the Hamburg City Health Study

Despite the association of regular coffee consumption with fewer neurodegenerative diseases, it remains unclear how coffee is associated with pre-clinical brain pathologies such as lesions in the white matter, degeneration of the cortex, or alterations of the microstructural integrity. White matter hyperintensities (WMH) are hyperintense lesions on T2-weighted images and are associated with an increased risk for stroke and depression, cognitive deterioration, and gait disorders [13,14,15]. As a marker of cerebral small vessel disease (CSVD) and vascular brain damage, WMH can vary in the degree of expression, depending on the age and the presence of cardiovascular risk factors, e.g., smoking or hypertension [16,17,18]. Previous studies have reported diverging results on the association of consumed coffee with imaging markers of CSVD. They found either beneficial associations of coffee with lacunar infarcts [7], beneficial [19] or detrimental [20] associations with WMH volume, or no significant associations at all [21,22].

A recently developed and valid imaging marker of microstructural integrity is the peak width of skeletonized mean diffusivity (PSMD), calculated as the distribution of the mean diffusivity (MD) between the 5th and 95th percentile in the white matter skeleton [23]. Only one study analyzed the association of coffee consumption with microstructural integrity, as quantified by fractional anisotropy, with a higher coffee consumption being associated with higher integrity of the white matter microstructure [24].

Damage to the brain structure is not restricted to white matter, but also extents to the cortex, e.g., in the form of atrophy. Except for one study focusing on the quantification of cortical thickness in regions susceptible for Alzheimer’s Disease [22], the link between coffee consumption and cortical thickness was only indirectly examined by measuring total brain volume or grey matter volume, with incongruent results between studies [7, 21,25,26]. This study aimed at investigating whether coffee consumption is associated with multiple brain MRI markers of vascular brain damage and neurodegeneration, including WMH, PSMD, and cortical thickness in a large, population-based cohort.

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