Male reproductive disorders are a growing issue due to the global decrease in sperm count and quality. Concerningly, chemicals like DEHP, which can be found in food storage containers, pharmaceuticals, and building materials, have been found to be one of the contributing factors. The toxicity of DEHP is due to its ability to mimic the hormones in our bodies, leading to long-term effects on health.

“The scientific community is aware of the fact that the current generation of men produce half as much sperm compared to the previous one,” said CheMyong Jay Ko (EIRH), a professor of veterinary medicine. “Although it is shocking, not much attention is paid to understanding the causes.”

The researchers used the Barker hypothesis as a guiding principle for their study. Proposed by the British physician and epidemiologist David Barker, the hypothesis argued that the nine months in utero are one of the most critical periods in a person’s life and can shape their future health trajectories.

“The Barker hypothesis primarily focuses on nutrition and we wanted to test whether the mother’s diet could change the health of the next generation,” Ko said. “Additionally, unlike the previous generation, we are constantly exposed to chemicals like DEHP, which can alter how our bodies function. We wanted to ask whether the exposure to both these factors can cause growing babies to have lesser functioning reproductive systems.”

In the past, both the Ko lab and other research groups have shown that prenatal exposure to DEHP decreases testosterone levels and causes fertility defects in male mice. Additionally, scientists have shown that maternal high-fat diet can also decrease sperm counts in male offspring. However, the effects of both together had not been studied.

The researchers used four groups of pregnant mice; one was a control and the other three were either exposed to DEHP, or a high-fat diet, or a combination of the two. They then followed each litter, which contained an average of 6 male and 6 female pups.

“Surprisingly, we found that a high-fat diet had a more damaging effect on the male reproductive systems compared to DEHP alone and the pups born from mothers who had been treated with both had the worst outcomes,” Ko said.

The researchers measured the weight of the body and different reproductive organs in pups during different stages of growth and puberty. They found that although the body weight of pups born from moms on a high-fat diet alone or in combination with DEHP was higher than the other pups, the weight of the reproductive organs was lower. They also found that these mice produced less sperm and had lower testosterone levels. By staining the tissues, the researchers found that the reproductive organs had abnormal cells, which were contributing to the gonadal dysfunction.

“In our studies, we used these mice as a model. Although we need to confirm these results in humans, this study should serve as a warning to our generation that we need to be careful about our environment and diet during pregnancy,” Ko said.

Depending on the threat, innate immune sensors can assemble complexes such as inflammasomes or PANoptosomes. The inflammasome can be thought of like an emergency broadcast system that is activated quickly, while the PANoptosome is more like an emergency response unit that generally integrates more signals and components to respond to the threat. How innate immune sensors work — what triggers them to act — has been a mystery, which researchers have been chipping away at for decades.

Nucleotide-binding oligomerization domain-like receptors (NLRs) are a large family of important molecules involved in inflammatory signaling. They are generally thought to function as innate immune sensors that detect threats. However, the specific roles of several NLRs in sensing are not yet understood. Scientists at St. Jude conducted a large screen, testing a specific NLR, NLRC5, to see what threats activate it. Through their efforts, they discovered that depletion of nicotinamide adenine dinucleotide (NAD), a molecule essential in energy production, triggers NLRC5-mediated cell death through PANoptosis.

“One of the biggest questions in the fields of immunology and innate immunity is what the various members of the NLR family are sensing, and what their functions are,” said corresponding author Thirumala-Devi Kanneganti, PhD, St. Jude Department of Immunology vice chair. “NLRC5 was an enigmatic molecule, but now we have the answer — it is acting as an innate immune sensor and cell death regulator, driving inflammatory cell death, PANoptosis, by forming a complex.”

Identifying the NLRC5 trigger

Scientists in the Kanneganti lab conducted a rigorous screen to get to the bottom of what threats trigger NLRC5. This included looking at pathogens such as bacteria and viruses, as well as pathogen associated molecular patterns (PAMPs) and damage associated molecular patterns (DAMPs) that can be released by or mimic an infection or the cause of an injury or illness, as well as other danger signals such as cytokines (immune signaling molecules).

The researchers also looked at heme, the component of hemoglobin responsible for carrying oxygen. Infections or disease can cause red blood cells to rupture in a process called hemolysis. This releases hemoglobin into the bloodstream. When hemoglobin breaks down into its components, it releases free heme, which is known to cause significant inflammation and organ damage. The researchers tested many different combinations of pathogens, PAMPs and DAMPs to see if NLRC5 was required for a response.

“Among all the combinations we tested, we identified that the combination of heme plus PAMPs or cytokines specifically induces NLRC5-dependent inflammatory cell death, PANoptosis,” said co-first author Balamurugan Sundaram, PhD, St. Jude Department of Immunology. “Our results showed for the first time that NLRC5 is central to responses to hemolysis, which can occur during infections, inflammatory diseases and cancers.”

Energy depletion triggers NLRC5 function

Upon identifying the heme-containing PAMP, DAMP and cytokine combinations that trigger NLRC5-dependent inflammatory cell death, the researchers further investigated how NLRC5 is regulated. They found that NAD levels drive NLRC5 protein expression. If NAD is depleted, that sounds an alarm that there is a threat the immune system should recognize. The researchers found that depletion of NAD is sensed by NLRC5, triggering PANoptosis.

“By supplementing with the NAD precursor, nicotinamide, we reduced NLRC5 protein expression and PANoptosis,” said co-first author Nagakannan Pandian, PhD, St. Jude Department of Immunology. “Therapeutically, nicotinamide has been widely studied as a nutrient supplement, and our findings suggest it could be helpful in treating inflammatory diseases.”

The researchers also discovered that NLRC5 is in an NLR network with NLRP12, which come together with other cell death molecules and form an NLRC5-PANoptosome complex that triggers inflammatory cell death. The finding builds on previous research by the Kanneganti lab showcasing the role of NLRP12 in PANoptosis.

A promising target for therapeutic development

NLRs are associated with diseases related to infection, inflammation, cancers and aging. This makes them intriguing targets for the development of novel therapeutics. The work of the Kanneganti lab shows that deleting Nlrc5 can provide protection against inflammatory cell death through PANoptosis and prevent disease pathology in hemolytic and inflammatory disease models, making NLRC5 an exciting therapeutic prospect.

“The fundamental knowledge that we have gained into how innate immune sensing works can be translated to numerous diseases and conditions,” Kanneganti said. “Aging, infectious disease, inflammatory disorders — things for which there are no targeted therapies, this could be an option.”

Authors and funding

The study’s other authors are Emily Alonzo, Department of Research and Development at Cell Signaling Technology; and Hee Jin Kim, Hadia Abdelaal, Omkar Indari, Roman Sarkar, Rebecca Tweedell, Jonathan Klein, Shondra Pruett-Miller and Peter Vogel, all of St. Jude, and Raghvendra Mall, formerly of St. Jude now of the Technology Innovation Institute, Abu Dhabi.

The study was supported by grants from the National Institutes of Health (AI101935, AI124346, AI160179, AR056296 and CA253095) and ALSAC, the fundraising and awareness organization of St. Jude.

Optical Sagnac interferometers are the most sensitive devices to rotations. They have been pivotal in our understanding of fundamental physics since the early years of the last century, contributing to establish Einstein’s special theory of relativity. Today, their unparalleled precision makes them the ultimate tool for measuring rotational speeds, limited only by the boundaries of classical physics.

Interferometers employing quantum entanglement have the potential to break those bounds. If two or more particles are entangled, only the overall state is known, while the state of the individual particle remains undetermined until measurement. This can be used to obtain more information per measurement than would be possible without it. However, the promised quantum leap in sensitivity has been hindered by the extremely delicate nature of entanglement. Here is where the Vienna experiment made the difference. They built a giant optical fiber Sagnac interferometer and kept the noise low and stable for several hours. This enabled the detection of enough high-quality entangled photon pairs such to outperform the rotation precision of previous quantum optical Sagnac interferometers by a thousand times.

In a Sagnac interferometer, two particles travelling in opposite directions of a rotating closed path reach the starting point at different times. With two entangled particles, it becomes spooky: they behave like a single particle testing both directions simultaneously while accumulating twice the time delay compared to the scenario where no entanglement is present. This unique property is known as super-resolution. In the actual experiment, two entangled photons were propagating inside a 2-kilometer-long optical fiber wounded onto a huge coil, realizing an interferometer with an effective area of more than 700 square meters.

A significant hurdle the researchers faced was isolating and extracting Earth’s steady rotation signal. “The core of the matter,” explains lead author Raffaele Silvestri, “lays in establishing a reference point for our measurement, where light remains unaffected by Earth’s rotational effect. Given our inability to halt Earth’s from spinning, we devised a workaround: splitting the optical fiber into two equal-length coils and connecting them via an optical switch.” By toggling the switch on and off the researchers could effectively cancel the rotation signal at will, which also allowed them to extend the stability of their large apparatus. “We have basically tricked the light into thinking it’s in a non-rotating universe,” says Silvestri.

The experiment, which was conducted as part of the research network TURIS hosted by the University of Vienna and the Austrian Academy of Sciences, has successfully observed the effect of the rotation of Earth on a maximally entangled two-photon state. This confirms the interaction between rotating reference systems and quantum entanglement, as described in Einstein’s special theory of relativity and quantum mechanics, with a thousand-fold precision improvement compared to previous experiments. “That represents a significant milestone since, a century after the first observation of Earth’s rotation with light, the entanglement of individual quanta of light has finally entered the same sensitivity regimes,” says Haocun Yu, who worked on this experiment as a Marie-Curie Postdoctoral Fellow. “I believe our result and methodology will set the ground to further improvements in the rotation sensitivity of entanglement-based sensors. This could open the way for future experiments testing the behavior of quantum entanglement through the curves of spacetime,” adds Philip Walther.

For an AI agent, this is easier said than done. Current approaches often utilize multiple hand-crafted machine-learning models to tackle different parts of the task, which require a great deal of human effort and expertise to build. These methods, which use visual representations to directly make navigation decisions, demand massive amounts of visual data for training, which are often hard to come by.

To overcome these challenges, researchers from MIT and the MIT-IBM Watson AI Lab devised a navigation method that converts visual representations into pieces of language, which are then fed into one large language model that achieves all parts of the multistep navigation task.

Rather than encoding visual features from images of a robot’s surroundings as visual representations, which is computationally intensive, their method creates text captions that describe the robot’s point-of-view. A large language model uses the captions to predict the actions a robot should take to fulfill a user’s language-based instructions.

Because their method utilizes purely language-based representations, they can use a large language model to efficiently generate a huge amount of synthetic training data.

While this approach does not outperform techniques that use visual features, it performs well in situations that lack enough visual data for training. The researchers found that combining their language-based inputs with visual signals leads to better navigation performance.

“By purely using language as the perceptual representation, ours is a more straightforward approach. Since all the inputs can be encoded as language, we can generate a human-understandable trajectory,” says Bowen Pan, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this approach.

Pan’s co-authors include his advisor, Aude Oliva, director of strategic industry engagement at the MIT Schwarzman College of Computing, MIT director of the MIT-IBM Watson AI Lab, and a senior research scientist in the Computer Science and Artificial Intelligence Laboratory (CSAIL); Philip Isola, an associate professor of EECS and a member of CSAIL; senior author Yoon Kim, an assistant professor of EECS and a member of CSAIL; and others at the MIT-IBM Watson AI Lab and Dartmouth College. The research will be presented at the Conference of the North American Chapter of the Association for Computational Linguistics.

Solving a vision problem with language

Since large language models are the most powerful machine-learning models available, the researchers sought to incorporate them into the complex task known as vision-and-language navigation, Pan says.

But such models take text-based inputs and can’t process visual data from a robot’s camera. So, the team needed to find a way to use language instead.

Their technique utilizes a simple captioning model to obtain text descriptions of a robot’s visual observations. These captions are combined with language-based instructions and fed into a large language model, which decides what navigation step the robot should take next.

The large language model outputs a caption of the scene the robot should see after completing that step. This is used to update the trajectory history so the robot can keep track of where it has been.

The model repeats these processes to generate a trajectory that guides the robot to its goal, one step at a time.

To streamline the process, the researchers designed templates so observation information is presented to the model in a standard form — as a series of choices the robot can make based on its surroundings.

For instance, a caption might say “to your 30-degree left is a door with a potted plant beside it, to your back is a small office with a desk and a computer,” etc. The model chooses whether the robot should move toward the door or the office.

“One of the biggest challenges was figuring out how to encode this kind of information into language in a proper way to make the agent understand what the task is and how they should respond,” Pan says.

Advantages of language

When they tested this approach, while it could not outperform vision-based techniques, they found that it offered several advantages.

First, because text requires fewer computational resources to synthesize than complex image data, their method can be used to rapidly generate synthetic training data. In one test, they generated 10,000 synthetic trajectories based on 10 real-world, visual trajectories.

The technique can also bridge the gap that can prevent an agent trained with a simulated environment from performing well in the real world. This gap often occurs because computer-generated images can appear quite different from real-world scenes due to elements like lighting or color. But language that describes a synthetic versus a real image would be much harder to tell apart, Pan says.

Also, the representations their model uses are easier for a human to understand because they are written in natural language.

“If the agent fails to reach its goal, we can more easily determine where it failed and why it failed. Maybe the history information is not clear enough or the observation ignores some important details,” Pan says.

In addition, their method could be applied more easily to varied tasks and environments because it uses only one type of input. As long as data can be encoded as language, they can use the same model without making any modifications.

But one disadvantage is that their method naturally loses some information that would be captured by vision-based models, such as depth information.

However, the researchers were surprised to see that combining language-based representations with vision-based methods improves an agent’s ability to navigate.

“Maybe this means that language can capture some higher-level information than cannot be captured with pure vision features,” he says.

This is one area the researchers want to continue exploring. They also want to develop a navigation-oriented captioner that could boost the method’s performance. In addition, they want to probe the ability of large language models to exhibit spatial awareness and see how this could aid language-based navigation.

This research is funded, in part, by the MIT-IBM Watson AI Lab.

However, a new study has shown it is not only humans that are using forms of the chemical as a way to help them survive challenging conditions.

In tests conducted on beaches in Cornwall, and in the laboratory at the University of Plymouth, scientists confirmed the findings of previous studies which showed large sandhoppers (Talitrus saltator) increase the levels of magnesium ions in their bodies as temperatures fall. This slows them down so they are less active than they would be during the warmer months.

However, the new study has shown for the first time that when they want to enter a period of deep sleep the creatures have the means through which to increase their magnesium levels even further — in some instances more than doubling them.

Essentially acting as a natural narcotic, the magnesium puts the sandhopper into a torpid state. This enforced rest means that the creatures stay hidden in burrows up to 30cm beneath the beach surface, without the need to come up for food or water, and to some extent buffered from the wintery conditions at the surface of the sand.

The study — which focused on the sandhopper population at Portwrinkle, in South East Cornwall — was carried out by Professor of Marine Zoology John Spicer and BSc (Hons) Marine Biology graduate Jack Bush.

Writing in the Journal of Experimental Marine Biology and Ecology, they say it sheds further light on why large sandhoppers seem to disappear from sandy beaches during cold winter weather.

Professor Spicer has spent decades studying the impact of temperature on marine and coastal species, including a number of studies on sandhoppers in Scotland.

He said: “It has been known for over a century that large sandhoppers, relatives of shrimp and crabs, can overwinter buried deep in the sand at the top of beaches away from the reach of the tide. What our study shows is that they may help themselves ‘go to sleep’ by allowing a natural narcotising agent, magnesium ions, to build up in their body fluids.

“Fluids containing magnesium, like Epsom salts, are routinely used by humans to relax but also when aquatic animals are being examined as part of scientific investigations. Our study shows nature has also found a way to do that without outside involvement.

“That said, as it’s a temperature dependent process it does raise questions over what will happen as our world warms. Will sandhoppers no longer sleep and just eat decomposing wrack all day long? Or will they change their sleeping habits by adapting the way they manage the magnesium in their body fluids?”

A paper just published in Scientific Reports- Nature by Sergio Dempsey as first author with colleagues Dr Soroush Safaei, Dr Gonzalo Maso Talou at Auckland Bioengineering Institute, along with co-author Dr Holdsworth (Mātai and FMHS & CBR at the University of Auckland), describes the new metric based on 4D flow MRI technology.

This innovative metric is particularly crucial because increased vascular pulsatility is linked to several brain conditions, including Alzheimer’s disease and other forms of dementia. By accurately measuring how pulsatility is transmitted in the brain, researchers can better understand the underlying mechanism of these diseases and potentiall guide development f new treatments.

Current MRI methods face limitations due to anatomical variations and measurement constraints. The new technique removes this issue by integrating thousands of measurements across all brain vessels, rather than looking one spot at a time as the traditional methods. This provides a richer metric representative of the entire brain.

“The ability to measure how pulsatility is transmitted through the brain’s arteries could revolutionise our approach to neurological diseases, and support research in vascular damage hypotheses” explained Mr. Dempsey. “Our method allows for a detailed assessment of the brain’s vascular health, which is often compromised in neurodegenerative disorders.”

The study also highlighted the potential to enhance clinical assessments and research on brain health. By integrating this new metric into routine diagnostic procedures, healthcare providers can offer more precise and personalised care plans for individuals at risk of or suffering from cognitive impairments.

In addition to its implications for patient care, the researchers have made their tools publicly available, integrating them into pre-existing open-source software. This enables scientists and clinicians worldwide to adopt the advanced methodology, fostering further research and collaboration in the field of neurology.

The research team is planning further studies to explore the applications of this technique in larger and more diverse populations, beginning with the “Digital Twin Dementia Study” starting at Mātai later this month. Results from the initial study of the metric also identified important sex differences in vascular dynamics which has initiated a new study focussing on sex-related dynamics which is anticipated to begin at Mātai and the Centre for Advanced MRI (CAMRI) in November.

Microscopically, magnetism arises from a collection of tiny magnets associated with electrons, called spin. In ferromagnetic materials, all the electron spins point in the same direction, while in antiferromagnetic materials, the electron spins are aligned in opposite directions, half pointing one way and half the other, canceling out the net magnetism. Altermagnetic materials are proposed in theory to possess properties combining those of both antiferromagnetic and ferromagnetic materials. One potential application of altermagnetic materials is in spintronics technology, which aims to utilize the spin of electrons effectively in electronic devices such as next-generation magnetic memories. However, identifying altermagnets has been a challenge.

An international research group led by Associate Professor Atsushi Hariki from the Graduate School of Engineering at Osaka Metropolitan University pioneered a new method to identify altermagnets, using manganese telluride (α-MnTe) as a testbed.

With the aid of a supercomputer, the researchers theoretically predicted a fingerprint of altermagnetism in X-ray magnetic circular dichroism (XMCD), which measures the absorption difference between left- and right-circularly polarized light. Then, using the Diamond Light Source synchrotron in England, they experimentally demonstrated the XMCD spectrum for altermagnetic α-MnTe for the first time in the world.

“Our results show that XMCD is an effective method for the simple identification of altermagnetic materials,” Professor Hariki said. “Also, it can be expected to further accelerate the application of altermagnets in spintronics.”