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Incredible Methods to Detect Parkinson’s Disease Years Earlier

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Parkinson’s disease is currently the fastest-growing neurological disorder in the United States; currently, 90,000 individuals have been diagnosed—a staggering 50% increase since the mid-1980s. The situation mirrors global trends, with an expected 25 million diagnoses by 2050, effectively doubled compared to today’s figures.

In summary, this is a significant issue. However, these numbers aren’t entirely surprising, considering longer life spans and growing populations. What is truly alarming, and frankly, unsettling, is how unprepared we are for this impending wave.

The available treatments are limited. Diagnostic tools are inadequate. Honestly, we still don’t really understand what causes Parkinson’s disease.

Yet, before you plunge into the depths of neurodegenerative despair, there is hope. Scientists worldwide are actively working to change the narrative surrounding Parkinson’s.

In particular, researchers are revolutionizing how we can detect Parkinson’s disease. Armed with cutting-edge technologies, AI, and a fundamentally evolving understanding of disease manifestation throughout the body, they’re aiming to detect it decades before any symptoms present themselves, rather than years.

Presently, there is no single definitive test for Parkinson’s disease. Instead, doctors diagnose it based on physical symptoms like tremors, slow movement, and muscle stiffness, often requiring assessments of tasks such as writing and speaking.

“Today’s neurodegenerative disease is what cancer used to be 50 years ago,” states Professor Hermona Solek, a leading researcher in next-generation diagnostic tools. “We often finalize a diagnosis only when all involved nerve cells are already dead, leaving us unable to properly treat the patient.”

But what if there were a way to diagnose Parkinson’s disease before it could do any significant harm? What if it could be caught on its way, before brain cells face irreversible damage?

This is no longer just a theory. In fact, there are multiple methods emerging.

AI Desk Accessories

Not all breakthroughs in diagnostics require a blood sample; some new innovations could be found right on your desk.

At the University of California, Los Angeles, Professor Junchen‘s lab claims to have developed a diagnostic pen that detects Parkinson’s disease by analyzing your writing.

This unique pen’s soft tip is crafted from an innovative magnetoelastic material that alters the magnetic field in response to pressure or bending—a phenomenon previously known in rigid metals but now applied to soft polymers, creating a new type of highly sensitive and user-friendly sensor.

“Utilizing magnetoelastic effects with soft materials represents a new operational mechanism,” Chen explains. “It can translate small biomechanical pressures, like arterial vibrations, into high-fidelity electrical signals.”

The pen, filled with magnetized ink, captures movements occurring both on paper and in the air, subsequently sending this data to a computer. Here, AI models analyze specific patterns linked to Parkinson’s motor symptoms.

Smart pens can be especially beneficial in countries where affordable diagnostic tools are needed—UCLA Jun Chen Lab

In a pilot study, the system successfully distinguished individuals with Parkinson’s disease from healthy controls with over 96% accuracy. Even better, Chen believes this pen can be mass-produced for merely $5 (£3.70).

“We have already filed for a patent and aim to commercialize this pen,” Chen states. “Simultaneously, we are working on optimizing it to improve our diagnostics’ accuracy.”

If handwriting isn’t your preferred method, Chen’s team has you covered. They’ve also created a Smart Keyboard utilizing the same principles.

This keyboard tracks subtle changes in pressure and rhythm as users type—often imperceptible to the naked eye—and relays that information to machine learning algorithms.

Initial tests indicate that it can identify characteristic motor abnormalities in Parkinson’s disease, and the team is combining this technology with a mobile app for continuous remote monitoring.

Together, these intelligent desk tools offer a glimpse into what Chen describes as the “personalized, predictive, preventive, participatory” future of Parkinson’s healthcare; a future where diagnosis is as simple as taking notes or sending emails.

This portable, soft keyboard employs magnetic elasticity to detect Parkinson’s disease and sends results to your smartphone—UCLA Jun Chen Lab

Parkinson’s Eye Test Detects Changes Two Decades in Advance

Picture diagnosing Parkinson’s disease during a routine eye exam, potentially decades before symptoms manifest. This is the promise of new non-invasive techniques developed by Victoria Soto Linan and her colleague at Laval University in Canada, using an established eye test known as electroretinography (ERG).

According to Soto Linan, this eye test serves as a “window to the brain,” as it’s part of the central nervous system. Issues like blurred vision and diminished contrast sensitivity manifest long before the well-known symptoms of tremors and stiffness.

The Soto Linan team collected data on how the retina responds to light flashes from both mice engineered to develop Parkinson-like symptoms and newly diagnosed human patients.

They identified unique retinal signals demonstrating “sick signatures,” particularly in women. Crucially, this weakened signal appeared in the mice prior to any behavioral disease signs.

This leads Soto Linan to believe that this eye test could detect Parkinson’s as much as 20 years before symptoms arise.

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And unlike other early diagnostic methods, this one is already well ahead of the game.

“ERGs are now employed in clinics to diagnose eye diseases,” she explains. “They also have the major advantage of being non-invasive.”

The patient sits before a dome that flashes lights, capturing how the retina responds. This could easily be integrated into a few minutes of your annual vision test.

The team is currently focusing on enhancing the testing process, with hopes of linking it to machine learning algorithms that will accelerate results, perhaps even making them portable to smartphones.

While the research is still in its early stages, its potential ramifications are enormous. As Soto Linan states, “This tool could identify at-risk individuals up to 20 years before symptoms emerge. Imagine how much less damage could be done by then.”

“Even if there is no treatment available, early intervention can often improve the quality of life in the long run.”

Detecting Parkinson’s Through Vocal Patterns

Can your voice indicate Parkinson’s disease before your physical body does? Recently, preprint research has explored whether AI can identify Parkinson’s simply by analyzing a person’s speech.

Around 90% of individuals with Parkinson’s develop motor speech disorders known as dysarthria, which can lead to issues like irregular pitch and breath control.

Globally, over 8.5 million individuals live with Parkinson’s disease—Getty

These vocal changes often arise earlier than more noticeable motor symptoms like tremors, thus serving as promising early indicators.

The research team collected brief audio recordings from 31 to 195 individuals, which included 33 individuals with the disease. Their data served to train four different AI models to recognize disease-related vocal patterns. When tested on new recordings from the same participants, the models identified Parkinson’s with an accuracy exceeding 90%.

These changes are subtle and occur early, and researchers suggest that speech-based assessments could provide low-cost, non-invasive diagnostic options.

Blood Tests for Diagnosing Parkinson’s

In April 2025, SOREQ and her colleagues—including her son—announced a groundbreaking new study.

The findings were surprising; they revealed a simple and inexpensive blood test utilizing PCR technology (remember this from COVID-19?) that can accurately detect Parkinson’s disease a few years prior to symptom onset.

This test functions by measuring the ratio between two markers that SOREQ and her team discovered in human blood.

Specifically, individuals with Parkinson’s exhibit abnormally high levels of certain molecules known as transfer RNA (tRNA) fragments, identifiable by a specific repeating pattern called conserved sequence motifs.

A new blood test can detect early Parkinson’s by analyzing the unique imbalance of small RNA molecules in your blood—Credit: Getty

Simultaneously, the team uncovered reduced levels of tRNA associated with mitochondria (the “powerhouses” of cells, responsible for producing most of your body’s energy) in the blood of Parkinson’s patients.

“We proposed that if there’s an increase in one sequence and a decrease in another, we could calculate the ratio and identify a probable diagnosis,” says Soreq.

If this ratio exceeds a specific threshold, it strongly indicates a diagnosis.

According to SOREQ, a traditional diagnosis of Parkinson’s can cost up to $6,000 (£4,400). The two PCR tests required for their method? Only $80 (£60).

“This is monumental. It makes a substantial difference,” she states. With some luck, the team anticipates this will become widely available within the next decade, potentially providing a crucial lifeline for patients globally.

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Source: www.sciencefocus.com

Biologists Discover How Plants Detect Heat During the Day

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A recent study conducted by the University of California reveals that plants utilize a variety of thermosensory systems, with sunlight-generated sugar playing a critical and previously overlooked role in their responses to daytime temperatures.

Arabidopsis plants showing growth in greenhouses. Image credit: Elena Zhukova/UCR.

“Textbooks traditionally assert that proteins like phytochrome B and early flowering 3 (ELF3) are primarily responsible for thermoregulation in plants,” noted Professor Chen.

“However, these theories are derived from data collected at night.”

“We aimed to explore the dynamics during the day when both light and temperature are elevated, reflecting the typical conditions most plants encounter.”

Professor Chen and his team conducted their research using Arabidopsis, a favored small flowering plant within the Institute of Genetics.

The researchers subjected the seedlings to temperatures from 12-27 degrees Celsius under varying light settings and monitored the elongation of hypocotyls, a classic indicator of growth response to warmth.

They discovered that phytochrome B, the photosensitive protein, could only sense temperature in low light. In bright conditions that mimicked midday sunlight, its ability to detect warmth was significantly inhibited.

Interestingly, plants continued to respond to heat, and their growth metrics remained elevated even when the thermosensory function of phytochrome B was curtailed.

“This highlights the existence of other sensory mechanisms,” Professor Chen remarked.

One significant observation stemmed from examining phytochrome B mutants that lacked thermosensory capabilities.

These mutants were only able to react to warmth when grown under light conditions.

In darkness, devoid of photosynthesis, they lost chloroplasts and did not exhibit increased growth in response to warmth.

However, their temperature response was restored upon reintroducing sugar to the growth medium.

“That was the point I realized that sugar does more than just promote growth; it serves as a signal indicating warmth,” Professor Chen explained.

Additional experiments demonstrated that elevated temperatures lead to the breakdown of stored starch in leaves, releasing sucrose.

This sugar stabilized a protein called PIF4, a crucial growth regulator. In the absence of sucrose, PIF4 would decompose rapidly, but its accumulation only occurred when another sensor, ELF3, became inactive and responded to heat.

“PIF4 requires two conditions: access to sugars and relief from suppression. Temperature facilitates both,” Professor Chen added.

This research unveils a complex network of systems. During daylight, when light serves as an energy source for carbon fixation, sugar-based mechanisms have evolved that enable plants to sense environmental changes.

As temperatures rise, stored starch transforms into sugar, permitting essential growth proteins to function.

The implications of these findings are noteworthy. As climate change brings about extreme temperatures, understanding the mechanisms plants use to sense heat may assist scientists in developing crops that thrive under increasingly unpredictable stress.

“This will transform our understanding of how plants perceive temperature,” Professor Chen remarked.

“It’s not merely about proteins activating or deactivating; it’s about energy, light, sugar, and more.”

“The results also emphasize the intricate sophistication found in the plant kingdom.”

“There’s a hidden intelligence in photosynthesis and the management of starch reserves.”

“When the moment arrives for them to reach for the sky, they do so with sweetness and precision.”

study published in the journal Natural Communication.

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D. Fan et al. 2025. Multi-sensor high temperature signaling framework for triggering daytime thermochemistry. Arabidopsis. Nat Commun 16, 5197; doi:10.1038/s41467-025-60498-7

Source: www.sci.news

Enhanced Cancer Screening Could Detect Early Cases in Women with Dense Breasts

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High-density breast tissue and tumors resemble each other on scans.

Golodenkov/Shutterstock

Recent research indicates that those with dense breast tissue may gain from an additional round of cancer screening, as a significant trial uncovered tumors that were overlooked in standard mammograms.

In the UK, mammograms—an x-ray scan used for breast cancer screening—are provided for individuals aged 50 to 71. These scans look for white spots that indicate cancer presence. However, around 50% of women in this age range have dense breasts, characterized by a high amount of fibrous and glandular tissue, also appearing white on the scans. This similarity complicates tumor detection.

“The challenge with dense breasts is that cancers may go unnoticed until they grow significantly large, which negatively affects prognosis,” said Thomas Hervich, who wasn’t a part of the study at the Medical University of Vienna in Austria.

To determine whether additional screenings can help, Sarah Vinnicombe and her colleagues at the University of Dundee recruited over 6,000 women aged 50-70 from across the UK. Participants were randomly divided into three groups, each receiving extra screening through advanced x-ray methods such as MRI, ultrasound, or contrast-enhanced mammography.

In this extended screening phase, MRI and contrast-enhanced mammography together identified 85 small tumors—three times as many as detected by ultrasound. Twelve of these tumors were located in milk ducts, suggesting a lower likelihood of spreading beyond the breast. Conversely, the other 73 tumors were invasive, increasing the risk that cancer could migrate into surrounding breast tissue and beyond.

“Detecting these cancers is crucial. They typically grow over time, and finding them within three to four years can lead to larger sizes,” stated Hervich. “Some tumors are aggressive, so I believe supplemental screening could save lives.”

However, it’s uncertain if this will hold true. For instance, a 2021 trial on ovarian cancer screening revealed a decrease in cases but did not correlate with increased longevity. Additionally, some tumors detected may not be cancerous or aggressive. Thus, unnecessary screening could lead to undue anxiety and treatment.

The researchers plan to continue monitoring participants to assess whether supplementary screenings result in saved lives.

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Source: www.newscientist.com

Astronomers Detect Compelling Evidence of Supermassive Black Holes in the Large Magellanic Cloud

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The mass of the ultra-large black hole in the heart of the large Magellan cloud, a small milky satellite galaxy, is approximately 600,000 solar mass.

Impressions of the Hyper Belt Lattist artist ejected from the large Magellan cloud (shown on the right). If the binary star system gets too close to an ultra-large number of black holes, intense gravity will tear the pair apart. One star is captured in tight orbits around a black hole, while the other is thrown outward at extreme speeds – often exceeding thousands of kilometers per second, making it a high-speed star. The inset diagram illustrates this process. The orbital path of the original binary is displayed as an interwoven line, one star is captured by a black hole (near the center of the inset), and the other is ejected into space (bottom right). Image credit: CFA/Melissa Weiss.

“Our Milky Way galaxy halo includes a few stars running faster than local escape speeds in orbit that carry them into intergalactic space,” said Dr. Jesse Han, Ph.D. of the Harvard & Smithsonian Center for Astrophysics and Colleagues.

“One mechanism for generating such ultrafast stars is the Hills mechanism. When a close binary star wanders near an ultrahigh Massive black hole, one star can be captured, while the other is ejected at a rate that reaches more than a second.”

In their new study, astronomers followed the path with ultrafine accuracy of 21 superfast stars in halos outside the Milky Way.

They confidently categorized these stars, finding that seven of them coincided with those born out of the center of the Milky Way.

However, the other nine stars coincided with those born from the centre of the large Magellan cloud, about 160,000 light years away from us.

“Cosmologically speaking, it's amazing to notice another super-large black hole just below the block,” Dr. Han said.

“Black holes are so stealthy that this has been under our noses this time.”

Researchers discovered a large Magellanic Cloud black hole using data from ESA's Gaia Mission.

They also used improved understanding of the orbital of the d-star galaxies around the Milky Way, which was recently obtained by other astronomers.

“We knew these superfast stars had been around for a while, but Gaia provided us with the data we needed to figure out where they actually came from,” says Dr. Kareem El-Badry, an astronomer at Caltech.

“Combining these data with a new theoretical model of how these stars move, we made this incredible discovery.”

“The only explanation we can come up with for these data is the presence of a monster black hole in the next Galaxy,” said Dr. Scott Lucchini, an astronomer at the Harvard & Smithsonian Center for Astrophysics.

a paper Reporting this finding is published in Astrophysical Journal.

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Ji Won Jesse Han et al. 2025. Hyper Belt Lattist tracks ultra-high Massive black holes in the large Magellan clouds. APJin press; Arxiv: 2502.00102

Source: www.sci.news

AI enhances radiologists’ ability to detect breast cancer in real-world exams

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Radiologists can benefit from AI assistance

Amelie Benoist/BSIP/Universal Images Group via Getty

Artificial intelligence models can actually help detect cancer and reduce the burden on doctors, according to the largest study of its kind. Radiologists who chose to use AI were able to identify an additional 1 in 1,000 breast cancers.

Alexander Katalinic and his colleagues at the University of Lübeck in Germany worked with about 200 board-certified radiologists to test an AI trained to identify signs of breast cancer from mammograms. Radiologists examined 461,818 women at 12 breast cancer screening centers in Germany between July 2021 and February 2023, allowing each woman to choose whether or not to use AI. As a result, 260,739 patients were examined by AI and a radiologist, and the remaining 201,079 patients were examined by a radiologist only.

Those who chose to use AI were able to detect breast cancer at a rate of 6.7 per 1000 scans. This is 17.6% higher than the 5.7 cases per 1000 scans for people who chose not to use AI. Similarly, when women diagnosed with suspected cancer underwent a biopsy, women diagnosed with AI were 64.5% more likely to undergo a biopsy in which cancer cells were found. Among women for whom AI was not used, the rate was 59.2%.

The scale of improvement in breast cancer detection with AI is “very positive and exceeded our expectations,” Katalinic said in a statement. “We were able to demonstrate that AI significantly improves cancer detection rates in breast cancer screening.”

“The goal was to show noninferiority,” says Stefan Bank of Vara, an AI company also participating in the study. “If we can prove that AI is as good as radiologists, it becomes an interesting scenario where we can reduce the workload. We were surprised that we were able to show an advantage.”

Over-reliance on AI in healthcare is a concern for some, as it risks missing signs of symptoms and could lead to a two-tiered treatment system where those who can pay are afforded the luxury of human touch. are. Radiologists spent less time examining scans that the AI ​​had already suggested were “normal,” meaning cancer was unlikely to be present, and scans that the AI ​​could not examine took an average of 16 seconds to examine. In contrast, there is some evidence that radiologists spend less time performing exams. Not classified. But these latest discoveries have been welcomed by those who specialize in the safe implementation of AI in healthcare.

“This study provides further evidence of the benefits of AI in breast cancer screening and should be a further wake-up call for policymakers to accelerate the adoption of AI,” she said. Ben Glocker At Imperial College London. “The results confirm what we have seen time and time again: With the right integration strategy, the use of AI is safe and effective.”

He welcomes the study's ability to empower radiologists to make their own decisions about when to use AI, and hopes to see more testing of AI in a similar way. . “This cannot be easily evaluated in the lab or in simulations, and instead we need to learn from real-world experience,” Glocker says. “The technology is ready. We need policies to follow now.”

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  • cancer /
  • artificial intelligence

Source: www.newscientist.com

AI system used to detect UK benefits fraud exposed for bias | Universal Credit

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The Guardian has uncovered that artificial intelligence systems utilized by the UK government to identify welfare fraud exhibit bias based on individuals’ age, disability, marital status, and nationality.

A review of a machine learning program used to analyze numerous Universal Credit payment claims across the UK revealed that certain groups were mistakenly targeted more frequently than others.

This revelation came from documents published under the Freedom of Information Act by the Department for Work and Pensions (DWP). A “fairness analysis” conducted in February of this year uncovered a significant discrepancy in outcomes within the Universal Credit Advance automated system.

Despite previous claims by the DWP that the AI system had no discrimination concerns, the emergence of this bias raises important questions about its impact on customers.

Concerns have been raised by activists regarding the potential harm caused by the government’s policies and the need for transparency in the use of AI systems.

The DWP has been urged to adopt a more cautious approach and cease the deployment of tools that pose a risk of harm to marginalized groups.

The discovery of disparities in fraud risk assessment by automated systems may lead to increased scrutiny of the government’s use of AI, emphasizing the need for greater transparency.

The UK public sector employs a significant number of automated tools, with only a fraction being officially registered.

The lack of transparency in the use of AI systems by government departments has raised concerns about potential misuse and manipulation by malicious actors.

The DWP has stated that their AI tools do not replace human judgment and that caseworkers evaluate all available information when making decisions related to benefits fraud.

Source: www.theguardian.com

University examiners unable to detect ChatGPT’s responses during actual examinations

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AI will make it harder for students to cheat on face-to-face exams

Trish Gant / Alamy

94% of university exam submissions created using ChatGPT were not detected as generated by artificial intelligence, and these submissions tended to receive higher scores than real student work.

Peter Scarfe Professors at the University of Reading in the UK used ChatGPT to generate answers for 63 assessment questions across five modules of the university's undergraduate psychology course. Because students took these exams from home, they were allowed to look at their notes and references, and could also use the AI, which they were not allowed to do.

The AI-generated answers were submitted alongside real students' answers and accounted for an average of 5% of all answers graded by teachers. The graders were not informed that they were checking the answers of 33 fake students, whose names were also generated by ChatGPT.

The assessment included two types of questions: short answers and longer essays. The prompt given to ChatGPT began with the words, “Include references to academic literature but do not have a separate bibliography section,” followed by a copy of the exam question.

Across all modules, only 6 percent of the AI ​​submissions were flagged as possibly not being the students' own work, although in some modules, no AI-generated work was ever flagged as suspicious. “On average, the AI ​​answers received higher marks than real student submissions,” says Scarfe, although there was some variability across modules.

“Current AI tends to struggle with more abstract reasoning and synthesising information,” he added. But across all 63 AI submissions, the AI's work had an 83.4% chance of outperforming student work.

The researchers claim theirs is the largest and most thorough study to date. Although the study only looked at studies on psychology degrees at the University of Reading, Scarfe believes it's a concern across academia. “There's no reason to think that other fields don't have the same kinds of problems,” he says.

“The results were exactly what I expected.” Thomas Lancaster “Generative AI has been shown to be capable of generating plausible answers to simple, constrained text questions,” say researchers at Imperial College London, who point out that unsupervised assessments involving short answers are always susceptible to cheating.

The strain on faculty who are tasked with grading also reduces their ability to spot AI cheating. “A time-pressed grader on a short-answer question is highly unlikely to come up with a case of AI cheating on a whim,” Lancaster says. “This university can't be the only one where this is happening.”

Tackling it at its source is nearly impossible, Scarfe says, so the education industry needs to rethink what it assesses. “I think the whole education industry needs to be aware of the fact that we need to incorporate AI into the assessments that we give to students,” he says.

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Source: www.newscientist.com

New research indicates that caterpillars are able to detect predatory wasps through the emission of static electricity.

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Predatory wasps are electrically charged and emit electric fields, and their larvae respond to these fields with defensive behaviour, according to a new study from the University of Bristol.

Sam J. England and Daniel Robert discovered that some terrestrial animals can sense the electric fields emitted by electrostatically charged predators and use this sensation to mount defensive actions. These photos show the four animal species examined in the study: (A) A Cinnabarga larva (Tilia jacobae) Taking a defensive posture. (B) The larva of a rare transpiration moth (Terrorcrus Rekens) in a defensive coiled position. (C) The larva of the European peacock butterfly (Aglais), (D) a predatory common hornet in the middle of a defensive maneuver (HornetImage credit: Sam J. England & Daniel Robert, doi: 10.1073/pnas.2322674121.

“Many animals naturally build up static electricity on their bodies as they move around in their environment, and we knew that static electricity can push or pull on other charged objects,” said researcher Sam England, from the University of Bristol.

“In particular, we knew that insect hairs can be moved by electric fields emitted by electrostatically charged objects, in the same way that an electrically charged balloon can move hair on the head.”

“This got us thinking: What if prey animals like caterpillars could detect predators by sensing the electric fields emitted by the predators?”

“Could the static electricity of a predator like a wasp be enough to alert the caterpillar to the approach of the wasp, by pushing and pulling on the caterpillar's sensory hairs?”

Dr England and his colleague, Professor Daniel Robert, from the University of Bristol, measured how much static electricity the wasps and caterpillars had picked up by passing them through a static sensor.

The researchers then fed these charge values ​​into a computational model to mathematically predict how strong the electric field would be as the wasp approached the larvae on the plant.

When the caterpillars reacted defensively to these conditions, they were able to determine whether it was sensory hairs that were detecting the electricity by using a laser to detect tiny vibrations and measuring how much the hairs moved in response to electric fields of different frequencies.

The results are concerning because they show that the caterpillars are also sensitive to the frequencies of electric fields emitted by power lines and other electronic devices.

This means that humans may be filling the environment with electrical “noise” that interferes with animals' ability to detect predators.

Dr England continued: “We now feel it is extremely urgent to assess whether introducing a new type of sensory pollution – electrical noise – is interfering with the ability of caterpillars, and other animals, to detect predators.”

Almost all terrestrial animals seem to accumulate static electricity, so this static sense may be widespread, and the discovery that static electricity plays a role in these ecological interactions would open up an entirely new dimension to our understanding of how animals sense each other, and more generally, how and why animals evolve in certain ways.

“Our study suggests that terrestrial animals may be able to use static electricity as a predator-detection cue,” Dr England said.

“This is likely an ability that is particularly widespread in insects and small animals such as spiders and scorpions.”

“This study provides the first example of an animal detecting predators by sensing static electricity emitted by the predator.”

“This reveals a new dimension of predator-prey interactions on land, but also suggests a previously unnoticed way in which we may be negatively impacting wildlife by introducing sources of electrosensory pollution.”

of study Published in Proceedings of the National Academy of Sciences.

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Sam J. England & Daniel Robert. 2024. Prey can detect predators via airborne electroreception. PNAS 121(23):e2322674121; doi:10.1073/pnas.2322674121

Source: www.sci.news

The fruit fly Drosophila melanogaster employs multiple senses to detect surrounding scents.

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Animals have various ways to detect chemicals in their environment, which differ depending on the species. Mammals use their tongues to taste, while fish and other aquatic creatures use their skin to taste. Insects, on the other hand, have taste buds not only inside their mouths but also outside their bodies.

Researchers have discovered that fruit flies, scientifically known as Drosophila melanogaster, have developed unique ways to utilize their senses of smell and taste to locate food and avoid dangers in diverse habitats. By exploring how fruit flies’ senses have evolved, scientists aim to uncover how these insects have adapted to their surroundings.

To study the sensory capabilities of fruit flies, researchers at the University of Lussanne in Switzerland compared the smells and tastes of different fruit fly species. They collected five essential body parts related to the flies’ senses: 1) larvae head, 2) egg-laying part, 3) front legs, 4) antennae, and 5) mouthparts with palpation structures. These body parts were collected from six closely related species of fruit flies living in various environments and consuming different diets.

The researchers separated male and female fruit flies into three replicates for each sex and species. They anesthetized the adult flies with CO2 to collect samples without causing harm. They separated larvae from their food source and removed their heads for analysis. This process was repeated three times for each body part of the adults, larvae, and egg-laying parts.

Using RNA sequencing technology, scientists examined the genes in different parts of the fruit fly’s body to understand how they respond to stimuli. This method helped identify active and inactive genes in various body parts, shedding light on how Drosophila adapts to its environment. The RNA data was stored in the Genomics Database for future research purposes.

The researchers observed that specific genes controlling smell and taste in fruit flies vary in their activation patterns. Changes in gene activity were influenced by factors like temperature, humidity, and interactions with other organisms. Differences in gene activity between male and female fruit flies were also noted, potentially impacting their mate selection.

The complexity of gene regulation in fruit fly sensory organs may vary across species and sexes, affecting their adaptation to diet and habitat changes. Further research is needed to understand the genetic basis of odor patterns in fruit flies and how it aids in their adaptation.

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Source: sciworthy.com

AI is able to detect the position of a mouse by analyzing its brain activity

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Micrograph of a cross-section of a mouse brain highlighting neural pathways (green)

Mark and Mary Stevens Neuroimaging and Informatics Institute/Scientific Photo Library

By analyzing a mouse’s brain activity, scientists can tell where the animal is and the exact direction the mouse is looking. With further research, the findings could one day help robots navigate autonomously.

The mammalian brain uses two main types of neurons for navigation. “Head direction cells” indicate where the animal is facing, and “grid cells” help provide her two-dimensional brain map of where the animal is located.

To learn more about the firing of these neurons, Vasilios Marlas and colleagues at the University of Tennessee, Knoxville, worked with the U.S. Army Research Laboratory to analyze data from previous studies.

In this experiment, probes were inserted into the brains of several mice. They then combined data about their neural firing patterns with video footage showing their position and head position as they moved around their open environment.

Because of this, Marlas and his colleagues developed an artificial intelligence algorithm that can figure out where the mouse is looking and where it is.

In practice, it’s similar to the drop pins and directional arrows on your smartphone’s map app, except instead of connecting to GPS satellites, scientists analyze the subjects’ brain activity.

“This method eliminates the reliance on updating GPS coordinates based on preloaded maps, satellite data, etc.,” Marulas says. “In a sense, the algorithm ‘thinks’ and perceives space in the same way as a mammalian brain.”

AI could eventually allow intelligent systems to move autonomously, he says. “In other words, we are taking advantage of the way the mammalian brain processes data and incorporating it into the architecture of our algorithms.”

Adam Hines Researchers from Australia’s Queensland University of Technology say the smartphone app analogy is helpful. “The location information (drop pin) and the direction (blue arrow) match, and during navigation, as he moves, the two pieces of information are constantly updated. Grid cells are like GPS, heading cells are It’s like a compass.”

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Source: www.newscientist.com

Utilizing New Technology to Detect Cancer Early: The Impact on Calderdale and Huddersfield NHS Foundation Trust in West Yorkshire

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A West Yorkshire NHS Trust is utilizing advancements in technology, such as artificial intelligence and surgical robots, to achieve crucial cancer targets and alleviate widespread pressure on hospitals.

Calderdale and Huddersfield NHS The Foundation Trust is meeting three important cancer targets established by the government.

These targets include a waiting time of 28 days for patients who receive an emergency referral and are diagnosed with an infection or cancer, a 31-day wait from the patient’s treatment decision to the first treatment, and a 62-day wait from the emergency GP referral to the first treatment.

Sky News was given a tour of the innovations behind the hospital’s results, starting with a diagnostic test called Cytosponge. The Cytosponge is a small capsule with a string attached that is swallowed by the patient. When dissolved in the stomach, a brush collects cells from the esophageal lining, which are then analyzed for abnormalities.

image:
New diagnostic test site sponge could help doctors find cases of esophageal cancer faster

Cytosponges are used as an alternative to longer and more invasive endoscopies. Patients find the cytosponge less invasive and report a quicker procedure time.

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Source: news.sky.com