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Comment by Dr. Krishna Kumari Challa 12 hours ago

Can a scent really sharpen your memory like people claim?
The sense of smell shares an intimate but mysterious connection with memory. We know that past experiences can flash vividly to mind if we smell a scent that accompanied them. This has led to claims that aromatherapy can enhance memory or even diminish cognitive decline. But the mechanisms by which the brain associates experience with scent are still being unravelled, and researchers got mixed results when tested to back up such claims.

New research suggests the answer is more complicated than you might think. The researchers found that young and older adults who completed memory tasks in rooms where lavender essential oil was diffused did not perform significantly better than people in unscented rooms.

But there was a catch: Older adults who encountered the scent during the first learning phase performed better than people who encountered it during the second. This suggests that novelty plays a role in binding the scent to experience, especially as we age. Perhaps it's not the type of scent but the newness of it that boosts memory.
Overall, the findings suggest that scent may be a more effective tool for supporting memory when used strategically, rather than as a general memory booster.

Renae Mannion et al., Olfactory context and memory across the adult lifespan: effects on mnemonic discrimination and olfactory function, Frontiers in Aging Neuroscience (2026). DOI: 10.3389/fnagi.2026.1917760

Comment by Dr. Krishna Kumari Challa 15 hours ago

Overall, the researchers found that some LLMs are more prone to changing their responses and choices when they are told they are being monitored. In their paper, they also sorted the models' written explanations for their choices into six categories.

When they were told that they might be monitored, models with larger compliance gaps often cited the prospect of retraining as a reason to follow the rule. When told they wouldn't be monitored, the same models more often discussed the tension between following the rule and expressing a value such as compassion.
The data collected by the authors and the code they used are available on   GitHub . In the future, other teams could potentially use the code to perform similar tests with other LLMs.

(HMMM! DON’T SOME HUMAN BEINGS DO THE SAME?)

Manodeep Ray et al., Alignment Faking Depends on the Value, Not Just the Model, arXiv (2026). DOI: 10.48550/arxiv.2611.03650

Part 2

Comment by Dr. Krishna Kumari Challa 15 hours ago

AI models can answer differently when told they are being watched

Large language models (LLMs), the AI systems underpinning the functioning of ChatGPT, Gemini and other similar platforms, are now widely used worldwide. While the responses provided by these models can be useful in many situations, they can also change under specific conditions.
Researchers at TCS Research and KIIT Bhubaneswar in India recently carried out an experiment with 10 different LLMs to understand the extent to which these models follow rules under different conditions. Their findings, reported in an arXiv preprint, suggest that some LLMs can change their responses when they are told that their answers might be monitored, yet how much their responses vary depends on the model and the value it is asked to set aside.

"Language models can follow a directive when they believe they are monitored and abandon it when they believe they are not, a pattern termed alignment faking," wrote the researchers in their paper.
Prior work establishes this for harmful-content objectives and a handful of moral-foundations values, leaving open how it distributes across a wider value space. The researchers argue it is value-dependent: whether compliance survives a change in perceived oversight is a property of the model-value pairing, not of the model alone.
LLMs were presented with everyday dilemmas and given two choices. One choice entailed following a rule and setting aside a particular value (e.g., sympathy), while the other allowed them to express the same value. The team looked at the choices the models made under three conditions: without a rule, with the rule and a cue indicating that their answers would not be monitored, and with the same rule and a cue indicating that their answers might be monitored.

The team performed tests with 32 different values or traits that could be expressed by the models, such as sympathy, autonomy, benevolence, compassion and resourcefulness.

The researchers measured how often each model chose the option that matched the rule in each of the three conditions. Across the 10 models, the gap between the monitored and unmonitored conditions ranged from about 5 to 50 percentage points when averaged across the 32 values.

The DeepSeek R1 Distill Llama 70B model exhibited the largest gap in compliance between monitored and unmonitored conditions. Its gap was more than double that of the much larger Mistral Large 2512 model.

In their analyses, the researchers accounted for measurement noise, the uncertainty that comes from estimating a model's behavior using a limited set of test questions. After accounting for this uncertainty, they found that about a quarter of the variation in the observed gaps in compliance was associated with the pairing of a model and a specific value.

During their tests, the researchers phrased rules differently for each value. Therefore, they were not able to fully separate the effect of a value from the effect of the wording they used.
Part 1

Comment by Dr. Krishna Kumari Challa 16 hours ago

Turning black-and-white patches into recognizable images
To investigate the mechanisms responsible for the speed with which the human brain learns to interpret ambiguous images, the researchers performed experiments involving 13 individuals with drug-resistant epilepsy. These people had electrodes implanted in their brains to monitor their seizures, and the researchers used those electrodes to record the activity of individual neurons.

While the participants performed a rapid learning and image recognition task, researchers recorded the activity of more than 1,000 neurons in their brains. The researchers then used machine learning methods to track what information was represented in their patterns of neuronal activity, and where and when this occurred.
The task completed by the participants required them to look at Mooney images and try to interpret them. Initially, the participants reported recognizing the objects in only 4% of trials.

They then showed participants the grayscale counterparts of those images. When they showed the black-and-white Mooney images again, participants immediately recognized the objects in the image. To study the mechanisms, they recorded the neuronal responses from visual and memory areas in the brain using the electrodes implanted in the patients' brains.

After one or a few learning sequences, during which participants saw a grayscale image followed by its Mooney counterpart, participants recognized the black-and-white images in 83% of trials, on average.

Moreover, once participants had seen the grayscale original, the patterns of neural activity elicited by the black-and-white Mooney image became more similar to those evoked by that original. These similar responses were observed in the occipital cortex, which processes visual information, and in parts of the medial temporal lobe, including the hippocampus, which plays a role in memory.

The researchers found that neurons in the visual cortex and in memory structures such as the hippocampus changed their patterns of activity after learning.
The visual cortex needed extra time to process the newly acquired information. This delay was then observed in memory structures, suggesting that the neocortex must first resolve the learned images before passing the information on.
Based on their findings, the researchers hypothesized that after rapid learning, recognizing Mooney images may involve signals passing back and forth within the neocortex, possibly with feedback from higher-order cortical areas. Medial temporal lobe responses appeared later, suggesting that feedback from this region may not drive the initial recognition of Mooney images.
In other words, neurons in the human brain change how they react to images in a single shot, reflecting the fast-learning process.

Marcelo Armendariz et al., Single-neuron encoding of rapidly learned visual information reshapes human perception, Nature Neuroscience (2026). DOI: 10.1038/s41593-026-02496-6.

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Part 2

Comment by Dr. Krishna Kumari Challa 16 hours ago

How the brain rapidly learns to interpret ambiguous images

Both humans and image recognition algorithms can take time to identify the objects and scenes depicted in some images. Emblematic examples of this phenomenon are Mooney images, pictures that are intentionally reduced to patches of black and white, with no shades of gray.

A lack of gray shades can make recognizing meaningful objects or scenes in Mooney images more challenging. When humans view them, they often struggle to interpret them at first, yet they can find that the images suddenly make sense after they see the grayscale originals.
Researchers recently tried to better understand the neural underpinnings of this rapid shift in perception. The results of their experiments, published in Nature Neuroscience, showed that after participants learned to recognize Mooney images, neurons in two brain regions responded to these images more like they responded to the grayscale originals.
This is a striking feature of human intelligence, our ability to learn so quickly that one brief glimpse can transform how we see things.

Sometimes we look at something that seems to make no sense, and then suddenly a single piece of information from our environment completely changes its meaning. For that to happen, the patterns of activity of the neurons in our brains must change to reshape our perception.
'Mooney' images (impoverished black-and-white images that are hard to interpret initially) is a paradigmatic example of rapid learning.
The effect is basically all or none. People cannot recognize these images initially and they learn to interpret them in one shot after exposure to the grayscale counterpart.

A Mooney image with its grayscale counterpart. Credit: Armendariz et al.

Part 1

Comment by Dr. Krishna Kumari Challa yesterday

Inflammation is involved in the earliest stages of atherosclerosis

Atherosclerosis begins decades before it causes a heart attack or stroke. During that time, arteries progressively accumulate fatty plaques and inflammatory cells without producing symptoms.
Inflammation plays an important role in the development of atherosclerosis from its earliest stages, long before the onset of symptoms or cardiovascular events such as heart attack or stroke. This is the main conclusion of a study led by a team at the Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC) and published in the European Heart Journal. The research shows that systemic inflammation contributes to disease development independently of traditional cardiovascular risk factors such as elevated cholesterol and hypertension.

In 3,471 adults without known cardiovascular disease followed for six years, systemic inflammation was associated with early atherosclerosis burden and progression independently of LDL cholesterol and blood pressure. White blood cell count was the inflammatory biomarker most closely associated with disease presence and progression.

The results show that inflammation contributes to the development of atherosclerosis independently of LDL cholesterol and blood pressure, two of the most important cardiovascular risk factors. The study also identifies white blood cell count as the inflammatory biomarker that best reflects the presence and progression of early atherosclerotic disease.
The research also shows that incorporating inflammatory biomarkers into cardiovascular risk assessment could complement the information provided by traditional risk factors and contribute to more personalized prevention.

Guiomar Mendieta et al., Inflammation and early atherosclerosis beyond traditional risk factors: an imaging study, European Heart Journal (2026). DOI: 10.1093/eurheartj/ehag846

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Comment by Dr. Krishna Kumari Challa yesterday

When a food-triggered reaction isn't a food allergy

It can be scary to have a reaction to something you've eaten and believe the symptoms indicate a food allergy. But for some, the cause of symptoms may be a different condition that can go unnoticed for years. Two new studies being presented at the American College of Allergy, Asthma and Immunology (ACAAI) Annual Scientific Meeting look at how eosinophilic esophagitis (EoE) diagnoses are on the rise and how EoE symptoms can be mistaken for a food allergy.

EoE is an allergic condition of the esophagus, the tube that carries food from your mouth to your stomach. In people with EoE, a type of white blood cell called an eosinophil builds up in the esophagus. This causes swelling and irritation. Over time, it can make it hard to swallow and can cause food to get stuck.

The first study looked at 29 adults who had been sent to an allergist because doctors thought they had experienced anaphylaxis, a severe allergic reaction, after eating certain foods. On average, each patient had gone to the emergency room three times, and most had been given at least one dose of epinephrine.

Their symptoms included a tight throat, trouble swallowing, chest pain, drooling and food getting stuck in their esophagus. But testing did not show a true food allergy. Instead, tissue samples taken from their esophagus showed that the patients had EoE. On average, it took more than five years for them to get the right diagnosis.
So not every frightening reaction after eating is anaphylaxis.
Eosinophilic esophagitis can closely mimic food allergy and may lead to repeated emergency visits before the correct diagnosis is made. When throat tightness, difficulty swallowing or food impaction recur, especially with negative food allergy testing, we should think about EoE. Early recognition can prevent years of uncertainty and progressive esophageal damage.

The second study used health records from more than 300 million patients in the U.S., making it the largest study of EoE in the country to date. Researchers found more than 500,000 people with an EoE diagnosis.

Key findings include:

The number of people diagnosed with EoE has increased about sevenfold since 2009.
EoE was most often first diagnosed in two age groups: teenagers and people in their 40s.
Males were more likely to have EoE than females.

People with several allergic conditions, such as asthma, hay fever, eczema and food allergies, were much more likely to have EoE. Among food allergies, wheat allergy had the strongest link.

When Anaphylaxis Is Not Anaphylaxis: Eosinophilic Esophagitis Mimicking Food Allergy, Annals of Allergy Asthma & Immunology (2026).

Epidemiology and Clinical Features of Eosinophilic Esophagitis: Analysis Using the COSMOS Database, Annals of Allergy Asthma & Immunology (2026).

Comment by Dr. Krishna Kumari Challa yesterday

Why we may feel the cold more as we age

Researchers from City of Hope have uncovered the biological reason why we become more sensitive to cold as we get older. Unlike white fat, which primarily stores energy, thermogenic fat is a special type of body fat that burns calories to produce heat and helps us stay warm. Thermogenic fat tends to become less active with age, which may explain why older people are more sensitive to cold.

How the body turns up the heat

The mix of thermogenic fat cells may give the body a built-in way to adjust to changing temperatures. When more warmth is needed, signals from the nervous and immune systems prompt less-active cells to increase heat production. When the need passes, the system can dial that activity back.

In older mice, however, this response became sluggish. Their thermogenic fat could still increase heat production, but it took longer. As a result, the animals had more difficulty maintaining their body temperature during cold exposure.

The researchers were able to restore some heat production in older mice by blocking one part of the biological pathway behind this slowdown. The treated animals produced more heat and burned more energy.
The drug used in the experiment, mifepristone, affects many parts of the body, so it is not a treatment for age-related cold sensitivity. But the results suggest that the decline in thermogenic fat activity could be reversed.

Anying Song et al., Bistable regulation of adipocyte heterogeneity governs thermogenesis, Nature (2026). DOI: 10.1038/s41586-026-11308-7

Comment by Dr. Krishna Kumari Challa yesterday

Parasites of parasites! Fungus infects powdery mildew

In the fungal kingdom, no one is safe from parasites—not even other parasites. A new study from North Carolina State University finds that Ampelomyces, an intracellular fungus that infects the plant disease powdery mildew, is far more common than previously known. By screening museum samples of powdery mildews up to about 150 years old, researchers discovered that not only is Ampelomyces quite common, but many specimens labeled as Ampelomyces are misidentified.

Because powdery mildews cannot be cultured, researchers screened more than 600 dried samples from the NC State herbarium (a collection of plant and/or fungal samples) to collect data on the prevalence of Ampelomyces. These samples were collected across 28 countries over the past 150 years and were analyzed alongside samples available on GenBank, a public database of DNA sequences used by scientists to share and study genetic data. Researchers found that about 7% of all powdery mildew specimens analyzed were infected by this parasite.

Understanding hyperparasites (parasites of parasites) could open doors to better control methods for the fungi they infect.

By understanding hyperparasite diversity and how many different strains are out there, it might give us insights on if some are better at controlling the fungus than others. There is a degree of host specificity to these hyperparasites, and if we are able to delve deeper they may be very useful in containing this plant disease.

Jacklyn Thomas et al., Parasites of parasites: Herbaria as archives of hidden ecological interactions, Proceedings of the Royal Society B: Biological Sciences (2026). DOI: 10.1098/rspb.2026.1772

Comment by Dr. Krishna Kumari Challa yesterday

Previous studies may also have underestimated how often important genes mutate.
We'd expect natural selection to rapidly remove mutations when they occur in more important genes, because these mutations are more likely to be harmful to the organism. This can create the illusion that important genes have fewer mutations when, in reality, some mutations have simply disappeared before we have a chance to analyze them—what you can think of as 'ghost mutations'."
To overcome these problems, the researchers used data from "mutation accumulation" experiments in E. coli and baker's yeast, designed to minimize the effects of natural selection. Three separate tests found no evidence that natural selection had substantially skewed the results.
The researchers then asked whether important genes really do mutate less often. They compared mutation rates with experimental evidence showing what happens when individual genes are removed. Some genes are essential, meaning that removing them kills the cell, while the loss of others has little or no effect.
The team also considered other ways of measuring a gene's importance, including how much of its protein a cell produces and the strength of natural selection acting on it over evolutionary time.

They found that genes producing more abundant proteins don't have lower mutation rates. Nor does the way genes evolve over thousands of years predict how frequently they mutate.
The researchers did, however, find that some features of the genome can affect mutation rates—for example, genes copied later during DNA replication tend to mutate more frequently.

Sofia Radrizzani et al., The importance of genes does not predict their mutation rate, PLOS Biology (2026). DOI: 10.1371/journal.pbio.3004089

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