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The family of Coronation Street actor John Saint Ryan, who played Charlie Whelan between 1993 and 1994, and sadly passed away at the age of 72 earlier this week, confirmed that he would not have a funeral as his body will be donated to science. John’s wife, Joyce, shared the heart-breaking news of his passing…
About the size of a 20 cent piece… the Kwonkan nemoralis. Credit: Dr Jeremy Wilson
Researchers discovered two new species of trapdoor spiders in northern Australia with uniquely engineered burrows, enhancing understanding of biodiversity in the region.
A team of scientists, led by The University of Western Australia, has identified two new species of trapdoor spiders in the Kimberley region, marking the first recorded discovery of this spider group in northern Australia.
The newly described species, Kwonkan fluctellus and Kwonkan nemoralis, were detailed in a recent publication in the Australian Journal of Taxonomy. Their discovery contributes significantly to the scientific understanding of Australia’s diverse and often unique spider fauna.
The research was led by Dr. Jeremy Wilson, a postdoctoral fellow in UWA’s School of Biological Sciences and a research associate at the Western Australian Museum. The spiders were discovered during a 2022 Bush Blitz expedition to the remote northern Kimberley, supported by the Australian Government.
Master architect… the silken collar around the spider’s burrow. Credit: Dr Jeremy Wilson
“We didn’t discover these spiders in the typical dry savannah landscapes the Kimberley is known for, but instead the specimens we found of Kwonkan nemoralis, which grows to around the size of a 20-cent coin, were located deep within a gorge that shelters patches of richer forest,” Dr Wilson said.
“These wetter forest patches are quite small and usually sheltered by the gorge so when you enter them, it’s a completely different environment – much more humid, with a different collection of plant species.”
Unique Microhabitats
According to Dr Wilson, what makes the spiders fascinating is their highly specialized burrow construction.
“Unlike most related species that build simple open entrances to their burrows, spiders of the Kwonkan genus create elaborate burrow entrances with unique features,” Dr Wilson said.
“We were exploring along a small creek when we noticed distinctive circular burrows in the sandy banks.
“The burrows constructed by the Kwonkan nemoralis had a little collapsible silken collar around the entrance, which had grains of sand embedded in it and were unlike anything we’d seen before – clever engineering that serves multiple functions.”
Rainforest found in sheltered regions of the gorge. Credit: Dr Jeremy Wilson
He continues, “When disturbed, the collar around the burrow’s entrance collapses and seals the entrance, while the sand blends perfectly with the surrounding landscape, making it virtually invisible to predators.
“One question we’re particularly interested in is why they build these unusual, modified entrances and whether they may be adaptions to specific hunting strategies, or for defending against predators such as scorpions, centipedes, and wasps, which we know hunt these spiders.”
Dr Wilson said the design may also protect the spiders during unexpected flooding events in arid areas.
The discovery of the two new species contributes to Taxonomy Australia, a national initiative under the Australian Academy of Science that aims to document Australia’s biodiversity within a generation.
Reference: “Two new species of the mygalomorph spider genus Kwonkan (Mygalomorphae: Anamidae) from the Kimberley region of Western Australia” by Jeremy D. Wilson, Michael G. Rix and Mark S. Harvey, 2025, Australian Journal of Taxonomy.DOI: 10.54102/ajt
About the size of a 20 cent piece… the Kwonkan nemoralis. Credit: Dr Jeremy Wilson
Researchers discovered two new species of trapdoor spiders in northern Australia with uniquely engineered burrows, enhancing understanding of biodiversity in the region.
A team of scientists, led by The University of Western Australia, has identified two new species of trapdoor spiders in the Kimberley region, marking the first recorded discovery of this spider group in northern Australia.
The newly described species, Kwonkan fluctellus and Kwonkan nemoralis, were detailed in a recent publication in the Australian Journal of Taxonomy. Their discovery contributes significantly to the scientific understanding of Australia’s diverse and often unique spider fauna.
The research was led by Dr. Jeremy Wilson, a postdoctoral fellow in UWA’s School of Biological Sciences and a research associate at the Western Australian Museum. The spiders were discovered during a 2022 Bush Blitz expedition to the remote northern Kimberley, supported by the Australian Government.
Master architect… the silken collar around the spider’s burrow. Credit: Dr Jeremy Wilson
“We didn’t discover these spiders in the typical dry savannah landscapes the Kimberley is known for, but instead the specimens we found of Kwonkan nemoralis, which grows to around the size of a 20-cent coin, were located deep within a gorge that shelters patches of richer forest,” Dr Wilson said.
“These wetter forest patches are quite small and usually sheltered by the gorge so when you enter them, it’s a completely different environment – much more humid, with a different collection of plant species.”
Unique Microhabitats
According to Dr Wilson, what makes the spiders fascinating is their highly specialized burrow construction.
“Unlike most related species that build simple open entrances to their burrows, spiders of the Kwonkan genus create elaborate burrow entrances with unique features,” Dr Wilson said.
“We were exploring along a small creek when we noticed distinctive circular burrows in the sandy banks.
“The burrows constructed by the Kwonkan nemoralis had a little collapsible silken collar around the entrance, which had grains of sand embedded in it and were unlike anything we’d seen before – clever engineering that serves multiple functions.”
Rainforest found in sheltered regions of the gorge. Credit: Dr Jeremy Wilson
He continues, “When disturbed, the collar around the burrow’s entrance collapses and seals the entrance, while the sand blends perfectly with the surrounding landscape, making it virtually invisible to predators.
“One question we’re particularly interested in is why they build these unusual, modified entrances and whether they may be adaptions to specific hunting strategies, or for defending against predators such as scorpions, centipedes, and wasps, which we know hunt these spiders.”
Dr Wilson said the design may also protect the spiders during unexpected flooding events in arid areas.
The discovery of the two new species contributes to Taxonomy Australia, a national initiative under the Australian Academy of Science that aims to document Australia’s biodiversity within a generation.
Reference: “Two new species of the mygalomorph spider genus Kwonkan (Mygalomorphae: Anamidae) from the Kimberley region of Western Australia” by Jeremy D. Wilson, Michael G. Rix and Mark S. Harvey, 2025, Australian Journal of Taxonomy.DOI: 10.54102/ajt
RFK Jt. Photo: sweejak via Flickr CC, Gonorrhea bacteria – Medical Illustrator: Alissa Eckert, CDC via Unsplash ” data-medium-file=”https://i0.wp.com/www.metroweekly.com/wp-content/uploads/2025/04/CDC-fires-entire-lab-working-on-STI-infections-RFK-Jt.-Photo-sweejak-via-Flickr-CC-Gonorrhea-bacteria.-Medical-Illustrator-Alissa-Eckert-CDC-via-Unsplash.jpg?resize=600%2C388&ssl=1″ data-large-file=”https://i0.wp.com/www.metroweekly.com/wp-content/uploads/2025/04/CDC-fires-entire-lab-working-on-STI-infections-RFK-Jt.-Photo-sweejak-via-Flickr-CC-Gonorrhea-bacteria.-Medical-Illustrator-Alissa-Eckert-CDC-via-Unsplash.jpg?fit=800%2C572&ssl=1″ class=”size-full wp-image-245737″ src=”https://i0.wp.com/www.metroweekly.com/wp-content/uploads/2025/04/CDC-fires-entire-lab-working-on-STI-infections-RFK-Jt.-Photo-sweejak-via-Flickr-CC-Gonorrhea-bacteria.-Medical-Illustrator-Alissa-Eckert-CDC-via-Unsplash.jpg?resize=800%2C572&ssl=1″ alt width=”800″ height=”572″>RFK Jr. Photo: sweejak via Flickr CC, Gonorrhea bacteria – Medical Illustrator: Alissa Eckert, CDC via Unsplash The Centers for Disease Control and Prevention (CDC) fired dozens of employees who…
Networking for the Future: Five Ways ESnet Accelerates the Nation’s Science – Science News Today – EIN Presswire
Florida dolphins are starving to death because of harmful marine algae blooms, researchers have said. In 2013, 8 percent of the bottlenose dolphins living in Florida’s Indian River Lagoon perished. Now, new investigations have revealed that the highly intelligent marine mammals may have starved because their hunting grounds were destroyed by a phytoplankton bloom produced by human activity.“We linked mortality and malnutrition to a decreased intake of energy following a shift in dolphins’ diets,” Dr. Charles Jacoby, of the Florida Flood Hub for Applied Research and Innovation, explained in a statement. “We linked the dietary shifts to changes in prey availability, and we connected changes in prey to system-wide reductions in the abundance of seagrass and drifting macroalgae. These reductions were driven by shading from an intense, extensive, and long-lasting bloom of phytoplankton,” he said.Researchers say a phytoplankton bloom was response for the deaths of dozens of Florida’s bottlenose dolphins in 2013. The bloom was driven by human activity
A new study has found why MRI scans may leave harmful metals behind in a person’s body.The University of New Mexico (UNM) study explored health risks caused by toxic rare earth metal gadolinium, which is used in MRI imaging.Gadolinium-based contrast agents, which create sharper images of the scan, are injected into the body before an MRI to explore any potential issues in the body.And while the metal is usually excreted from the body, and most people experience no adverse side effects, previous research has shown some gadolinium particles have been left behind. These particles have been found in the brain, kidney, and even in the blood and urine years after an MRI.According to the US Food and Drug Administration (FDA) the main adverse health effect related to gadolinium retention is a condition called nephrogenic systemic fibrosis (NSF), found in patients with pre-existing kidney failure.NSF can cause a thickening and hardening of the skin, heart and lungs—and cause painful contracting of the joints.The FDA has logged reports of adverse events involving multiple organ systems in patients who had had normal kidney function, but a causal association between these events and gadolinium retention could not be established.Now the new study, led by UNM professor Brent Wagner, MD, has found a connection between gadolinium and oxalic acid, a molecule found in foods which binds with metal ions, leading to medical issues such as kidney stones.
Pictured: Stock image of a man undergoing an MRI scan, as medics discuss a chart in the background.
Pictured: Stock image of a man undergoing an MRI scan, as medics discuss a chart in the background.
Vladislav Stepanov/Getty Images
The research team used test tube experiments and found that oxalic acid caused tiny amount of gadolinium to precipitate from the contrast agent and form nanoparticles, which infiltrated cells of different organs.Oxalic acid also forms in the body when people eat foods or take supplements containing vitamin C, and Wagner said in a statement that he “wouldn’t take vitamin C if I needed to have an MRI with contrast because of the reactivity of the metal.”He said a person’s “metabolic milieu” may determine whether a patient forms the nanoparticles.”It might be if they were in a high oxalic state or a state where molecules are more prone to linking to the gadolinium, leading to the formation of the nanoparticles,” he said. “That might be why some individuals have such awful symptoms and this massive disease response, whereas other people are fine.”In their study, it was found that almost 50 percent of the patients with gadolinium traces in the body had only been exposed to the contrast agent one time, meaning there was “something that is amplifying the disease signal.”The nanoparticle formation “might explain why there’s such an amplification of the disease. When a cell is trying to deal with this alien metallic nanoparticle within it, it’s going to send out signals that tell the body to respond to it.”Wagner’s team is currently researching ways to identify who may be at the biggest risk from gadolinium contrast agents, by building an international patient registry including blood, urine, fingernail hair samples to gather evidence of gadolinium accumulation in the body.Do you have a tip on a science story that Newsweek should be covering? Do you have a question about medicine? Let us know via science@newsweek.com.ReferenceHenderson, I. M., Benevidez, A. D., Mowry, C. D., Watt, J., Bachand, G. D., Kirk, M. L., Dokładny, K., DeAguero, J., Escobar, G. P., & Wagner, B. (2025). Precipitation of gadolinium from magnetic resonance imaging contrast agents may be the Brass tacks of toxicity. Magnetic Resonance Imaging, 119. https://doi.org/10.1016/j.mri.2025.110383
Behind every intelligent system is a powerful mix of artificial intelligence (AI), machine learning (ML), and data science. Understanding how these technologies work together is key to unlocking their potential in finance, healthcare, retail, and beyond.
The Evolution of AI: From Rules to Reasoning
Artificial Intelligence, at its core, refers to machines that simulate human behavior and cognitive functions. The earliest AI systems were rule-based. Imagine a robot instructed to exit a room: “Walk two steps forward, turn left, walk three more steps.” These kinds of commands rely on pre-programmed logic—rigid, predictable, and effective for limited tasks. Classic examples include early chess computers that followed decision trees with pre-determined strategies.
But real intelligence doesn’t just follow rules—it adapts. That’s where machine learning comes in.
Machine Learning: The Future
Machine learning marked a paradigm shift. Rather than relying on explicit programming, ML systems learn from data. For example, spam filters today don’t just block emails containing the word “lottery.” Instead, they analyze thousands of signals from millions of examples to improve over time.
Deep learning takes this a step further. Using artificial neural networks inspired by the human brain, these models process vast datasets to perform complex tasks—such as image recognition, voice transcription, and real-time translation—with remarkable accuracy.
At the frontier lies Generative AI. Unlike previous models that analyze existing content, generative AI creates entirely new material: text, images, music, even software code. Tools like GPT-4 and DALL·E exemplify how AI can be both analytical and creative.
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The Role of Data Science: Making Sense of the Noise
So where does data science fit in? Think of data science as the connective tissue between AI technologies and real-world application. It’s the discipline of extracting insights and knowledge from structured data (like spreadsheets) and unstructured data (like emails, images, or sensor feeds).
Data science involves multiple stages: collecting and cleaning data, analyzing it for patterns, visualizing the findings, and applying them to solve problems. It also requires a blend of technical skills, mathematical understanding, and—most critically—domain expertise.
For example, a data scientist working in healthcare doesn’t just need to know how to build a model that predicts patient readmissions. They need to understand which predictions are clinically useful. A model that forecasts whether a patient will be readmitted in 15 years may be accurate—but it’s not actionable. A model predicting 15-day readmission, on the other hand, could directly influence post-discharge care.
The Democratization of Data Science
Until recently, data science was reserved for those with deep coding and mathematical expertise. Today, thanks to tools like TensorFlow, Keras, and low-code platforms, it’s possible to build sophisticated models with just a few lines of code—or even no code at all.
This democratization has broadened access to AI, allowing professionals with domain knowledge—but not necessarily a PhD in computer science—to contribute meaningfully to AI development. While technical skills still add value, they are no longer the gatekeeper.
You can have the most powerful model in the world, but if it’s not aligned with business needs, it’s not useful. Moreover, domain knowledge helps identify data anomalies. For instance, if a patient record says they are 300 years old, a savvy data scientist might recognize that the input was likely the birth year (e.g., 1725) entered incorrectly, not an actual age.
Machine Learning 101: How Models Learn
Training a machine learning model is a bit like teaching a child. Initially, the model knows nothing. It is exposed to training data—say, past home sales with variables like number of bedrooms, bathrooms, square footage, and final sale price.
The model starts making predictions (e.g., estimating the price of a house). When it’s wrong—and it usually is at first—it learns by comparing its output to the real price and adjusting its internal calculations. Over time, it becomes more accurate. The process is measured using metrics like “mean absolute error” (how far off the predictions are, on average).
In this scenario, price is the “dependent variable”—the outcome we’re trying to predict. The number of bedrooms, square footage, and other features are the “independent variables” or “inputs.”
Supervised vs. Unsupervised Learning
Most machine learning applications fall into two categories: supervised and unsupervised learning.
In supervised learning, the model learns from labeled data. For example, a dataset of home prices where each row includes the final sale price helps the model learn direct correlations. Within supervised learning, regression problems predict continuous values (like a house price), while classification problems predict discrete categories (like fraud/no fraud).
Unsupervised learning, by contrast, deals with unlabeled data. Instead of predicting outcomes, it finds hidden patterns. One common technique is clustering, which groups similar data points together. For example, clustering customer data might reveal natural groupings based on behavior, which can inform targeted marketing strategies.
Dimensionality Reduction: Seeing the Invisible
Real-world data often comes with many features—sometimes hundreds. Visualizing such data is a challenge. That’s where dimensionality reduction techniques like PCA (Principal Component Analysis) come in. They compress high-dimensional data into two or three dimensions so humans can better interpret it.
For example, word embeddings in natural language processing might represent each word in 200 dimensions. Projecting this into 2D space helps us visualize how words relate to each other semantically. Words like “increase” and “optimize” cluster together, while “book” and “story” form another group—capturing contextual meaning.
The Foundation: Data Systems and Engineering
None of this is possible without robust data systems. Whether you’re working with real-time stock data, hospital records, or weather sensors, the infrastructure to collect, store, and manage data is essential.
Data engineers play a crucial role here. They build the pipelines that extract, transform, and load data (ETL), ensuring it is clean, consistent, and ready for analysis. If the data isn’t reliable, the insights derived from it won’t be either.
At the top of the data science pyramid is decision-making. Whether it’s a business choosing to expand into a new market, a hospital deciding on treatment protocols, or a public policy team crafting regulations, data-driven insights are transforming how we make decisions.
But with great power comes great responsibility. While AI can automate tasks and uncover insights at unprecedented speed, human judgment is still essential—especially when it comes to ethics, context, and accountability.
Final Thoughts: A New Era of Intelligence
Artificial intelligence is no longer just a buzzword—it’s the engine driving competitive advantage across industries. Yet its success hinges on more than just algorithms. It requires clean data, thoughtful design, deep domain understanding, and a commitment to responsible use.
As the tools continue to evolve and barriers to entry fall, one thing is clear: the future won’t be built by AI alone. It will be shaped by the people who understand how to harness it.
For more on Forbes, check out: AI’s Growing Role In Financial Security And Fraud Prevention or Risk-Based Authentication: The Future Of Secure Digital Access.
Scientists, professors, engineers, teachers and doctors are routinely ranked among the most trustworthy people in society. This is because these professions rely heavily on research, and good research is viewed as the most reliable source of knowledge.
But how trustworthy is research? Recent news from the US suggests that the Trump administration wants to fund more “reproducibility studies”.
These are studies that check to see if previous results can be repeated and are reliable. The administration’s focus seems to be specifically on studies that revisit the debunked claim of a link between vaccines and autism.
This is a worrying waste of effort, given the extensive evidence showing that there is no link between vaccines and autism, and the harm that suggesting this link can cause. However, the broader idea of funding studies that attempt to repeat earlier research is a good one.
Take research on Alzheimer’s disease as an example. In June 2024, Nature retracted a highly cited paper reporting an important theory relating to the mechanism of the disease. Unfortunately, it took 18 years to spot the errors and retract the paper.
If influential studies like this were regularly repeated by others, it wouldn’t have taken so long to spot the errors in the original research.
Alzheimer’s is proving a particularly tricky problem to solve despite the large amounts of money spent researching the disease. Being unable to reproduce key results contributes to this problem because new research relies on the trustworthiness of earlier research.
More broadly, it has been known for almost ten years that 70% of researchers have problems reproducing experiments conducted by other scientists. The problem is particularly acute in cancer research and psychology.
The Trump administration wants to fund more ‘reproducibility studies’.
Joshua Sukoff/Shutterstock
Research is difficult to get right
Research is complicated and there may be legitimate reasons research findings cannot be reproduced. Mistakes or dishonesty are not necessarily the cause.
In psychology or the social sciences, failure to reproduce results – despite using identical methods – could be due to using different populations, for instance, across different countries or cultures. In physical or medical sciences problems reproducing results could be down to using different equipment, chemicals or measurement techniques.
A lot of research may also not be reproducible simply because the researchers do not fully understand all the complexities of what they are studying. If all the relevant variables (such as genetics and environmental factors) are not understood or even identified, it is unsurprising that very similar experiments can yield different results.
In these cases, sometimes as much can be learned from a negative result as from a positive one, as this helps inform the design of future work.
Here, it is helpful to distinguish between reproducing another researcher’s exact results and being given enough information by the original researchers to replicate their experiments.
Science advances by comparing notes and discussing differences, so researchers must always give enough information in their reports to allow someone else to repeat (replicate) the experiment. This ensures the results can be trusted even if they may not be reproduced exactly.
Transparency is therefore central to research integrity, both in terms of trusting the research and trusting the people doing the research.
Unfortunately, the incentive structure within research doesn’t always encourage such transparency. The “publish or perish” culture and aggressive practices by journals often lead to excessive competition rather than collaboration and open research practices.
One solution, as new priorities from the US have suggested, is to directly fund researchers to replicate each other’s studies.
This is a promising development because most other funding, alongside opportunities to publish in the top journals, is instead linked to novelty. Unfortunately, this encourages researchers to act quickly to produce something unique rather than take their time to conduct thorough and transparent experiments.
We need to move to a system that rewards reliable research rather than just novel research. And part of this comes through rewarding people who focus on replication studies.
Industry also plays a part. Companies conducting research and development can sometimes be guilty of throwing a lot of money at a project and then pulling the plug quickly if a product (such as a new medicine) seems not to work. The reason for such failures is often unclear, but the reliability of earlier research is a contributing factor.
To avoid this problem, companies should be encouraged to replicate some of the original findings (perhaps significant experiments conducted by academics) before proceeding with development. In the long run, this strategy may turn out to be quicker and more efficient than the rapid chopping and changing that occurs now.
The scale of the reproducibility, or replicability, problem in research comes as a surprise to the public who have been told to “trust the science”. But over recent years there has been increasing recognition that the culture of research is as important as the experiments themselves.
If we want to be able to “trust the science”, science must be transparent and robustly conducted.
This is exactly what has happened with research looking at the link between vaccines and autism. The topic was so important that in this case the replication studies were done and found that there is, in fact, no link between vaccines and autism.