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Temperature shifts change plant proteins powering photosynthesis

Humans adjust to changes in temperature by putting on a sweater or taking off layers. Plants adjust to temperature changes, in part, by switching the way they express the protein that performs the critical first step of photosynthesis, according to new research from Cornell, Texas A&M and Stockholm University.

Rubisco is the most abundant protein on Earth, and it is responsible for fixing carbon so that plants can convert it into photosynthetic energy. Better understanding of the basic science underpinning rubisco’s function, therefore, has implications for increasing agricultural yields, improving carbon sequestration technology and understanding how plants may adapt to a warming climate.

In the paper “ Rubisco Kinetic Acclimation at the Holoenzyme Level,” published April 15 in Proceedings of the National Academy of Sciences, the researchers demonstrate that while rubisco’s protein core remains consistent, parts of its exterior can be swapped out, akin to an outfit. A stiffer exterior is preferred in the heat, for protection, and a looser one in the cold, to increase efficiency. This study, using the mustard-family plant Arabidopsis, is the first to show how rubisco acclimates to temperature changes in any plant species.

The Gravity Particle Should Exist. So Where Is It?

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Physics is this close to understanding the entire universe. And what lives in this gap? Many physicists think it’s the elusive graviton—the quantum particle of gravity—whose discovery will finally allow us to stitch together our two great theories of nature into a single master theory. But what is the graviton, and does it even exist?

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The immunoproteasome disturbs neuronal metabolism and drives neurodegeneration in multiple sclerosis

Now online! The immunoproteasome disturbs neuronal metabolism and drives neurodegeneration in multiple sclerosis: (Cell 188, 4567–4585.e1–e12; August 21, 2025)


Now online! (Cell 188, 4567–4585.e1–e12; August 21, 2025)

During post-publication review of our article, we, the authors, identified several errors in figure assembly and annotation affecting representative images and sample size reporting. These issues are limited to figure presentation and do not affect the underlying data, quantification, or conclusions of the study.

In Figure 2G, incorrect representative images were inadvertently used for the interferon-γ-OE and PSMB8-OE glutamate conditions. The correct images have now been inserted.

Automated Imaging Differentiation for DementiaIncluding Alzheimer Disease Dementia and Dementia With Lewy Bodies

Two most common causes of dementia in older adults are Alzheimer disease dementia (ADD) and dementia with Lewy bodies (DLB).1,2 Differentiating between ADD and DLB in the clinical environment remains challenging with high rates of misdiagnosis using the current standard of care.2 Up to 50% of neuropathologically confirmed DLB, known as Lewy body disease (LBD), are correctly diagnosed antemortem, with ADD as the most common misdiagnosis.2,3 Distinguishing DLB from ADD is a vital part of patient care as DLB has a worse prognosis and requires different treatment plans compared with ADD.4 Patients with DLB are particularly sensitive to neuroleptics prescribed in dementia care, leading to worsening cognitive and motor functions.5 Further, new disease-modifying therapies are approved for ADD, but not for DLB.6,7

The National Institute on Aging and Alzheimer’s Association developed a research framework for Alzheimer disease (AD) classification using biomarkers such as amyloid, tau, and neurodegeneration.8 Amyloid positivity, as assessed using PET or biofluid assays (e.g., AB42/40, ptau217), is a core pathologic, distinguishing feature of AD. However, amyloid and Lewy body copathologies occur in over 50% of patients with LBD and can contribute to diagnostic uncertainty.2,9,10 In lieu of a DLB biomarker classification framework, current diagnostic criteria recommend combining indicative and supportive biomarkers to improve distinguishing between DLB and ADD. Indicative biomarkers include dopamine transporter scans (DaTscan), myocardial scintigraphy, and polysomnography. Supportive biomarkers are collected using MRI, PET, or SPECT scans, and EEG. Current MRI biomarkers in DLB leverage the relative sparing of the medial temporal lobe (MTL) to aid in differentiation.

Fat cells steer flies away from pathogen-tainted food through a newly revealed neural circuit

If humans or animals eat something that causes them to feel unwell, they subsequently avoid this food source. Until now, it has been unclear precisely how this avoidance learning takes place. A new study shows that communication between the brain cells and fat cells could play a crucial role here. The participants from the Universities of Bonn and Tohoku (Japan) and University Hospital Bonn have revealed the previously unknown mechanism in the fruit fly Drosophila. It may also exist in a similar form in mammals and even in humans. The results have now been published in the journal Neuron.

Anyone who’s ever had an upset stomach after eating a bad meatball knows just how much this experience can put you off them. Within research, this is also known as “conditioned taste aversion”: The brain registers the immune response to the bacteria and their toxins and concludes from this that the food source should be avoided in the future.

It is not yet known how the immune system’s discovery of the pathogens leads to a change in behavior. “As this learned food avoidance can be found in all species, we investigated this question in a model organism – the fruit fly Drosophila,” explains Prof. Dr. Ilona Grunwald Kadow. “Within this model, we can clarify how the brain and body interact with each other to trigger an avoidance reaction that is vital for survival.”

Phage Therapy Gains Momentum in Antibiotic Resistance

As antimicrobial resistance continues to challenge traditional treatment protocols, bacteriophage therapy is emerging as a viable precision medicine alternative. Recent clinical developments demonstrate the potential of these virus-based interventions to target multi-drug resistant pathogens while preserving the host microbiome.


Growing antimicrobial resistance is prompting renewed interest in phage therapy, with preliminary data indicating improved outcomes when combined with standard antibiotics.

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