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Dr. Hristofor Kirchev (Bulgaria)

Department of Crop Science, Faculty of Agronomy, Agricultural University – Plovdiv, Bulgaria.


Dr. Md. Mamun Habib (Malaysia)

School of Quantitative Sciences (SQS), College of Arts and Sciences (CAS), University Utara Malaysia (UUM).


Dr. Salah Ismaeel Yahya (Iraq)

MW and Communication Engineering, Director, Quality Assurance, Koya University, Iraq.


Dr. Li Junhui (China)

School of Mechanical and Electronical Engineering, Central South University, China.


Dr. Karim. H. Hassan (Iraq)

Dean Assistant for Scientific Affairs, College of Science of University of  Diyala, Iraq.


Dr. Florin Negoescu (Romania)

Department of Machine Manufacturing Technology, “Gheorghe Asachi” Technical University of IAŞIRomania.


Dr. Ujjwal Pyakurel (Nepal)

Kantipur Dental College Teaching Hospital and Research Center, Nepal.


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In the new study, published in July in the journal Nature, Huang and colleagues demonstrated that cancer-detecting AI might work better when it takes this humanized approach.

Training AI to hunt for cancer

AI algorithms called vision language models (VLMs) struggle with the first step that Huang described — that initial, cursory scan. That's in part because many pathology AI systems learn from what pathologists leave behind at the end of that search: a labeled image pointing out where the cancer is or an official diagnosis.

Instead, the researchers trained their new AI on pathologists' search behavior. They called this approach to training "Pathology-CoT," short for "chain of thought." It turns observable actions, including where pathologists move around and zoom in on an image, into training data.

To collect the data, the team created a tool that recorded how pathologists moved around a slide and changed magnification. The raw logs, gathered from eight pathologists, were messy, as a given pathologist might drift across a slide, overshoot their intended region of focus or fiddle with magnification to adjust it to their liking.

To clean up the data, the researchers filtered out those incidental movements, focusing on moments that appeared to represent deliberate attention, such as lingering over one view or making a sustained pan. Then, they compared those regions with eye-tracking data to check that the software was capturing where pathologists were actually looking.

For each region a pathologist inspected, the VLM also drafted a short rationale explaining why the region was worth examining and what features were visible; human pathologists could then accept, edit, or reject the rationale, creating additional training data for the AI. In one example, the AI flagged a portion of a slide as potentially metastatic and suggested zooming in to look for atypical cells. Other inspected regions were flagged as healthy tissue.

Ultimately, the researchers used this training method to build a new tool called Pathology-o3. It scans a slide at low resolution, uses a model trained on pathologists' behavior to choose regions worth a closer look, then sends higher-resolution views of those regions to a VLM for analysis.

Pathology-CoT trains algorithms to scan over a whole slide and then return to regions of interest for a closer look. (Image credit: Universal Images Group via Getty Images)

Putting it to the test

Huang said the goal of the new study was not to show that Pathology-o3 worked better than specialized AI models that are specifically built to detect specific types of cancer; those models are often trained disease by disease. Rather, the researchers wanted to see whether their new training approach could help a general-purpose AI navigate a pathology slide more effectively.

They compared Pathology-o3 to other general-use AI systems, such as OpenAI's o3, and asked the algorithms to examine slides containing lymph node tissue. These slides were collected from colorectal cancer cases and some contained metastatic cancer, which human pathologists had already labeled.

The algorithm correctly identified slides that were positive for cancer 100% of the time. However, of the slides it identified as positive, 15.5% were actually negative. By comparison, OpenAI o3 correctly identified slides that were positive for cancer 87.5% of the time. Of the slides it identified as positive, 53.3% were actually negative.

The researchers designed Pathology-o3 to err on the side of flagging something for another look, rather than potentially missing cancer. That might help to explain the rate of false positives, Huang said.

Whether that rate of false alarms is acceptable depends on how Pathology-o3 is used, said Mohammad Asadi, a data scientist at Stanford University who was not involved in the research. It's not precise enough for the AI to diagnose patients on its own, but it could still be useful for a system to point a human toward regions of a slide that are worth double-checking. It may be an advantage that the approach shows the pathologist a specific region to inspect rather than declaring an entire slide suspicious, he said.

The researchers tried repeating the test on an independent dataset that the algorithms hadn't seen before to see how well it worked on unfamiliar slides. Pathology-o3 correctly identified slides that were positive for cancer 97.6% of the time. Of the slides it identified as positive, 37.1% were actually negative. The finding is an example of how AI performance can change when the data source changes, even when the medical task stays the same.

Asadi explained this result suggests the system can still work with slides from a different source. But that result does not yet show that using this tool would make pathologists more accurate or efficient in practice.

Can it help pathologists?

The researchers applied their training approach to several existing VLMs, finding that the models' performance consistently improved after the training. That suggests that the navigation data from pathologists was useful across settings, Asadi said.

For Huang, that is the most important result. "The takeaway isn't our system," he said. "It's that the missing ingredient has been sitting in hospitals this whole time."

The study did not compare Pathology-o3 directly with human pathologists, but the researchers said that wasn't their aim.

"The right question isn't whether it beats a pathologist," Huang said. "It's whether a pathologist working with it catches more [cancer cases] and works faster." The current study did not address the latter question, either, but the team's next experiment is designed to test pathologists on the same cases with and without Pathology-o3, measuring what they catch and how long they take to do so.

The system's most plausible use is as a prescreening tool, Asadi said, but he stressed that the research has not yet shown that doctors who use it become faster or more accurate. Asadi wants an even tougher test: trials conducted across multiple hospitals that measure not just accuracy and speed but also pathologists' workloads. He wants the trails to assess the burden of false alarms from the AI algorithms and whether doctors recognize when the AI is wrong.

Importantly, cancer diagnoses can require information from multiple slides, stains and a patient's medical history, while the current system just reads one slide at a time. "I wouldn't claim it should diagnose on its own," Huang said.

This article is for informational purposes only and is not meant to offer medical advice.

'> AI trained to 'think' like human pathologists may be better at spotting cancer

Celestron StarSense Explorer 130

(Image credit: Zane Landers)

The DX 130 rides on the same alt-azimuth mount/tripod shared with the other StarSense Explorer DX models. It’s reasonably sturdy, with one major caveat: you can’t actually point the DX 130 straight up. When aimed above about 70 to 75 degrees, the tube crashes into the mount or the tripod legs.

However, there is a simple workaround here. Deliberately set the tripod up unlevel, which lets you reach the zenith without affecting the mount or the StarSense system at all. It looks a little silly, but it works. The tripod is fairly stable as long as you’re not in a windy spot. Loading the accessory tray with sandbags or diving weights, or filling the hollow aluminum legs with sand or foam, helps considerably if you’re willing to do a bit of DIY work.

The mount has slow-motion controls with small gears for fine adjustments. They have a fair amount of play in them, but since this telescope is really only meant to be used up to about 200x magnification or so, it doesn’t matter much. In practice, the author barely used the slow-motion controls and simply nudged the scope by hand. For aiming the DX 130AZ, a red dot finder is provided in addition to the StarSense Explorer tech.

The StarSense Explorer DX 130 AZ comes with two eyepieces: a 25mm (26x) and a 10mm (65x). These are three-element eyepieces, likely Konigs or reversed Kellners. They work acceptably; the 25mm is a little blurry at the edges, and the 10mm shows some chromatic aberration toward the edge, but both are reasonably sharp. The bigger problem is that the housings are all plastic and there are a lot of internal reflections as a result.

Celestron StarSense Explorer 130

(Image credit: Zane Landers)

Better aftermarket eyepieces would be a wise investment, both for improved performance and a wider range of magnifications. We’d recommend a 6mm redline (108x), a 2x Barlow of some sort and a good wide-field eyepiece like the 24mm APM/StellaLyra UFF (27x), if you can afford it — the 6mm redline gets a bit closer than the provided 10mm eyepiece and offers a much sharper view, while the 24mm UFF is far crisper than the stock 25mm. With a Barlow lens and the 6mm, you can go up to 216x on the moon, planets and double stars — about the highest magnification this telescope can allow. Or you can choose your own eyepiece set to suit your needs.

The StarSense Explorer DX 130’s focuser is a 2.5-inch rack-and-pinion unit with a metal drawtube and plastic body. There’s some play in it, but it works acceptably well as long as you don’t overload it with too much weight. The focuser has adapters to accept both the provided 1.25” eyepieces as well as aftermarket wide-field 2-inch eyepieces, but at f/5, you really need a coma corrector of some sort to use the latter, which, along with the eyepieces themselves, will set you back quite a bit.

Being a Newtonian reflector, the DX 130 will usually need to be collimated for sharp images. Celestron doesn’t include a collimation tool, which is mildly annoying, but a basic collimation cap is cheap, easy to 3D-print, or can be improvised from a film canister. Overall, the DX 130 is lightweight and very portable, which is one of its real strengths — it’s easy to carry about, and the StarSense Explorer app will have no trouble plate-solving, even if you have to pick the scope up and carry it around the yard to avoid trees or other obstructions.

Celestron StarSense Explorer DX 130AZ: Performance

Celestron StarSense Explorer 130

(Image credit: Zane Landers)

The DX 130 shows a great deal of detail on the moon, down to features a few kilometers across, and it’s genuinely sharp. The focuser is a little coarse, but with some finesse it will reach focus even at high magnifications. The 130mm f/5 optics can handle up to about 250x magnification on a night of steady atmospheric conditions, though the best lunar and planetary views are usually found somewhere between 150-220x and the mount starts to get wobbly as you push towards the higher end of that range.

On the planets, the DX 130 handles shadow transits of Jupiter’s moons easily, shows all four Galilean moons as disks and even reveals the Great Red Spot. Saturn’s rings are obvious, along with a smattering of a half-dozen moons, and the Cassini Division is visible when the rings are tilted more open than they are at present. Uranus is also visible as a tiny greenish disk, though its moons are not. It’s a struggle to distinguish Neptune from a star at all, but the StarSense Explorer app will help you identify which blue dot it is in a sea of similar looking 8th-magnitude points.

Celestron StarSense Explorer 130

(Image credit: Zane Landers)

One practical note: you do need to keep your phone screen dim and red while using this telescope with the StarSense Explorer app. The app has a red mode, but notifications, or your phone simply reverting to the lock screen, can flash the screen bright, and even at its dimmest a phone screen can affect your eyes’ dark adaptation quite a bit. But for most people this won’t be a serious problem if the screen is dimmed fully and used with a proper red filter — either a software solution or a physical red film.

As for deep-sky objects, the spiral arms of the Andromeda Galaxy and M33 look fantastic under a dark sky, though under light pollution only their cores are visible. Globular clusters such as M13 and M3 begin to resolve, while more difficult targets are harder going — M53 was tough from Bortle 5 but doable from Bortle 2 conditions. Under a bright sky, open clusters like M11, M35 and the Pleiades are the main deep-sky attractions with this telescope; under a dark sky, fainter nebulae and galaxies shine far better while the strands of dark nebulae and galactic cirrus begin to show up around open clusters.

The brightest planetary nebulae start to resolve with the DX 130 regardless of sky conditions. Most observers won’t see much color in them with this scope’s relatively small aperture — though some people pick out a bluish-green tint in objects like the Ring Nebula and the Cat’s Eye Nebula — but regardless, they’re reasonably easy to see even from light-polluted areas, especially with an aftermarket nebula filter.

Celestron StarSense Explorer DX 130AZ: Functionality

Celestron StarSense Explorer 130

(Image credit: Zane Landers)

The StarSense Explorer DX 130 is, by design, a wide-field telescope, and one of its appeals is that you don’t strictly need any computerized pointing aids at all. The nice thing about the StarSense Explorer system is that it’s entirely optional — you can aim the telescope manually, with or without your phone docked, just like any other manual scope.

Set-up is quick: assemble the tripod and mount, attach the tube and an eyepiece, dock your phone, run the app’s alignment routine, and you’re observing. It’s much easier to get started with the StarSense Explorer than with a GoTo telescope, and you aren’t required to use it, either — for a quick gander at the moon or Jupiter for instance, there’s really no need to deal with the phone dock and app. With experience you might find yourself eventually not needing the StarSense Explorer tech at all, especially given the telescope’s wide field of view, which is quite forgiving of any manual pointing errors.

Should you buy the Celestron StarSense Explorer DX 130AZ?

On the value for money front, you could get a 6-inch or even an 8-inch Dobsonian for the price of the DX 130 if you’re happy with a fully manual scope, or you could look at one of the other options below. The DX 130 is more portable than a full-size 6- or 8-inch Dobsonian and has a much wider field of view with any given eyepiece — but if you don’t have a fairly dark sky, that advantage is somewhat less valuable compared to those telescopes’ considerably larger aperture, which helps reveal more on targets less affected by light pollution.

A reflector like this gives much better views than the cheap refractors sold at this price and offers a much wider possible field of view than catadioptrics. The StarSense Explorer DX 102, effectively this scope’s sister model, has too much chromatic aberration to deliver sharp high-power images and doesn’t gather as much light as the 130. The StarSense Explorer 5-inch Schmidt-Cassegrain is decent optically and is admittedly a little more forgiving of cheap eyepieces thanks to its longer focal ratio, but it’s no better for planetary work than the DX 130 with proper eyepieces, can’t deliver a wide deep-sky field of view, and gathers slightly less light overall.

Overall, the DX 130 wouldn’t be the author’s top recommendation, but it’s certainly in the top 10 for the price range. If you’ve already decided this is the kind of telescope that you want, it’s a perfectly good choice; just consider the Dobsonians first if you want a manual telescope, or a fully automatic GoTo instrument if you want something more high-tech.

If this product isn't for you

'> Celestron StarSense Explorer DX 130AZ review

However, at a blistering 900 degrees Fahrenheit (480 degrees Celsius), Venus tops Mercury's 800 F (430 C) highest surface temperature, despite being an average of 31 million miles (50 million kilometers) farther from the sun. So how can the second planet from our star be hotter than the closest planet to it?

It all comes down to reflectivity, atmospheric composition and geological history, experts told Live Science.

Totally different atmospheres

A planet's distance from its star is not the only factor that influences the planet's temperature.

"Distance tells us how much sunlight arrives at a planet, but it does not tell us how much is reflected … absorbed, how efficiently heat escapes, or how effectively the atmosphere transports heat around the planet," Stephen Kane, an astrophysicist who studies planetary habitability at the University of California, Riverside, told Live Science in an email. "Those properties can be just as important as distance, and sometimes much more important."

Mercury makes the case in miniature. According to Kane, the planet has essentially no atmosphere, so incoming sunlight strikes bare rock directly, heating it to extreme temperatures during the day. But with barely anything overhead to trap that warmth, Mercury radiates it straight back into space the moment the sun sets. As a result, nighttime temperatures plunge from roughly 800 F (430 C) during the day to about minus 290 F (minus 180 C) at night ‪—‬ a swing of well over 1,000 degrees, he added.

Venus tells the opposite story. Wrapped in an atmosphere that's roughly 90 times as dense as Earth's and consists almost entirely of carbon dioxide, Venus traps heat so effectively that its surface temperature barely changes at all, no matter where the sun happens to be, Kane explained.

A permanent blanket

A series of small black objects behind the yellow planet of Venus.

Venus' thick, sulfuric-acid clouds reflect roughly three-quarters of incoming sunlight back into space. (Image credit: NASA / Handout via Getty, NASA / Handout via Getty)

Sunlight arrives at a planet mostly as near-infrared radiation and visible light, which pass through atmospheres consisting primarily of gases such as nitrogen, oxygen and carbon dioxide with relatively little trouble. Once the ground and lower atmosphere absorb that energy, though, they re-release it as infrared radiation, the kind of energy we feel as heat. Carbon dioxide happens to be excellent at grabbing and holding on to infrared radiation, Kane noted.

On Venus, the atmosphere is so deep and loaded with carbon dioxide that infrared energy leaving the surface gets absorbed and re-emitted over and over before any of it finally escapes into space. Some of that energy gets redirected back downward, Kane said, further warming the planet's lower atmosphere and surface. However, the heat isn't trapped forever, since energy conservation means that Venus must eventually release the same amount of energy it absorbs.

Notably, Venus doesn't actually soak up more sunlight than Mercury overall. Thick cloud cover reflects roughly three-quarters of incoming sunlight back into space before it ever reaches the ground, and only about 3% of the sunlight that arrives at Venus makes it down to the surface, Kane said. In fact, if that atmosphere and cloud deck were stripped away, a bare-rock Venus would actually run cooler than Mercury, since it would be receiving only about 29% as much sunlight from the start, he explained. It's the blanket, not the sunbathing, that makes Venus the hotter world.

Where the blanket came from

Venus' atmospheric blanket didn't spring into existence all at once, and how it formed is its own open question. Venus shows strong evidence of past and present volcanic and tectonic activity acting to smother the planet in greenhouse gases, Paul Byrne, a planetary scientist and associate professor of Earth, environmental and planetary sciences at Washington University in St. Louis, told Live Science in an email.

This volcanic activity has been so intense that Bryne describes modern Venus as sitting in a "post-runaway greenhouse" state, where its extreme heat has become a self-sustaining process regardless of what the planet's interior is doing at any given moment.

The heat isn't being generated from below; it's a consequence of the atmosphere Venus already has, locked in place by the same infrared-trapping effect described above.

Taken together, the experts' answers point to the same underlying lesson: How close a planet sits to its star is only the opening chapter of its climate story. What kind of atmosphere it has and how effectively that atmosphere holds on to heat are usually responsible for the rest.

See how well you know our planetary neighborhood with our solar system quiz!

'> Why is Venus hotter than Mercury, when Mercury is closer to the sun?

Brigid Lynch, a geomorphologist with the California-based hydrology consulting firm Balance Hydrologics, found the molar this summer while surveying a creek bed. "As a geologist, I've gone out and seen stuff in the field, but definitely nothing as exciting as this," Lynch said in the statement.

Lynch's team was working on a habitat enhancement project to support the threatened California red-legged frog (Rana draytonii) inside the preserve, which is located on the San Francisco Peninsula in San Mateo County. The fossil was a complete surprise and dates to at least 10,000 years ago, when the Pacific mastodon (Mammut pacificus) is thought to have gone extinct.

Mastodons are extinct, elephant-like creatures that lived during the last ice age. (Image credit: Midpen (left); Balance Hydrologics (right))

The Pacific mastodon was first recognized as a species in 2019. It was closely related to the American mastodon (Mammut americanum). Both elephant-like creatures lived in North America during the Pleistocene epoch (2.6 million to 11,700 years ago), but the Pacific mastodon inhabited regions farther west than its cousin, roaming parts of present-day Mexico, California, Oregon and the northern Rocky Mountains of the western U.S., according to the statement.

Pacific and American mastodon fossils reveal slight differences between the species, including a narrower third molar, thicker hind-leg bones and occasionally absent lower-jaw tusks in the Pacific mastodon, according to the Royal Alberta Museum.

The newfound tooth is "another puzzle in the Ice Age saga that we can put into place," Thompson said. To get a precise date for the fossil, Midpen donated it to Stanford University's Doerr School of Sustainability in July, the Los Angeles Times reported.

Researchers will produce a 3D copy of the molar that Midpen plans to use for public outreach and education, according to the statement.

'> 'Oddly shaped rock' unearthed in California turns out to be 'immaculate' mastodon molar

The study, which hasn't been peer-reviewed yet, investigated how a roughly 60% decline in the Atlantic Meridional Overturning Circulation (AMOC) would impact global food production. The AMOC is a system of ocean currents that regulates the global climate and brings heat from the tropics to the Northern Hemisphere.

However, the system is currently the weakest it's been in more than 1,000 years, having lost 10% to 20% of its strength due to climate change, estimates show.

In the new study, researchers used a computer model to simulate the AMOC's future decline. They modeled 100 years of change, assuming 3.6 degrees Fahrenheit (2 degrees Celsius) of warming above preindustrial levels. The team forced an AMOC slowdown over the first 50 years in one simulation but didn't modify the circulation in a second experiment, which enabled them to compare the world with and without an AMOC failure.

The model's outcomes mirrored the results of previous studies, showing broad cooling in the Northern Hemisphere and a southward shift of the belt of clouds and rain that encircles the globe near the equator. As in past studies, these effects were irreversible on human timescales, said study co-author Michael Hinge, a senior economist at the nonprofit Alliance to Feed the Earth in Disasters (ALLFED).

"Once the damage is inflicted, it stays there," Hinge told Live Science, adding that in the 50 years after the peak rate of AMOC slowdown in the model, the circulation weakened by an extra 20%. The study was posted on ALLFED's website July 7.

An agricultural "shock"

Hinge and his colleagues ran three models to look at the most common varieties of wheat, maize and rice, layering these models on top of the AMOC simulations. They found that global yields for these crops decreased by 5.3% by the 50-year mark, once the AMOC had weakened substantially.

"Lots of these effects manifest earlier, so it's a moving target," Hinge said.

He added that the results were based on a single warming scenario that was run once and used monthly rather than daily data, so they should be interpreted as a proof of concept. "We've set up a pipeline that is ready to take pretty much any AMOC scenario that can generate high-resolution climate data and see what it implies for crops," Hinge said.

While there was a 5.3% decrease overall, that global metric concealed huge differences between regions. For example, countries like Germany and Ukraine lost up to 19% of their cereal yields due to sudden, sharp cooling, while Canada showed a 12% decline in production for the same reason.

"Scandinavia, the United Kingdom and Canada are particularly exposed," Hinge said, "but there are many other [vulnerable] locations, some of which may be surprising even to the people who are living there."

For instance, the model produced a "very nasty brown splodge" of reduced precipitation over India, Pakistan and big parts of China, he said. Iran, Sudan and Eritrea, which are already water-stressed countries, also exhibited severe declines in rainfall, indicating that a portion of the agriculture there may not survive an AMOC collapse.

The 5.3% result also hides variability among years, Hinge said, because it's an average. So there may be years when global cereal losses reach 10% or more, which would bump up food prices, in part because countries might stockpile crops instead of trading them.

Global response

The regional effects of an AMOC slowdown would interact with climate change, triggering unpredictable outcomes. In particular, maladaptation — taking actions that end up backfiring — is a worry, Hinge said.

"You're preparing and setting up for much hotter temperatures or trying to develop areas for these temperatures and the trends you're seeing, and suddenly you see something different," he said. "That may cause an even larger compounding effect [on agriculture]."

The findings build on prior research led by Paul Ritchie, a climate scientist at the University of Exeter in the U.K., whose team explored the effects of an AMOC collapse on agriculture in Great Britain. That study, published in 2020, had similar limitations as the new one, so it's best to interpret both papers as a suggestion of what might happen rather than a precise forecast, Ritchie told Live Science in an email.

"I think the new study is valuable in showing the potential scale and geographic distribution of the initial agricultural shock," said Ritchie, who was not involved in the latest work. "What that would ultimately mean for food production and food security is more uncertain, because there are many possible responses by farmers, markets and governments." For that reason, the regional predictions may be more informative than the 5.3% global average, he said.

An AMOC collapse could reduce rainfall over the Indian subcontinent, crippling agriculture in this region. (Image credit: Bhaswaran Bhattacharya/IndiaPictures/Universal Images Group via Getty Images)

Average food prices worldwide could rise by 17% to 40% if the AMOC weakens to the extent it did in the model, the study predicts. The level of increase depends on how countries respond to the shock and their degree of preparedness, as well as on whether agriculture can be sustainably expanded in places like the African Sahel (the semi-arid zone directly south of the Sahara Desert) and Brazil, the analysis suggests.

The U.S. would not be spared from this price jump, and the model indicated that the country could see crop losses of about 3% for domestic wheat and corn. This loss would likely impact other foods that rely on cereal inputs, like meat, Hinge said. "The variety of foods available in the stores may well diminish for periods," he added.

An AMOC collapse would fundamentally deepen the need for countries to trade with each other, because some regions — such as the Indian subcontinent, China, Iran, Russia, Ukraine and Canada — could endure this giant shift only if they imported more food, Hinge said.

"Should countries turn inwards and start to restrict exports in an effort to ensure access for their own citizens first, or deal with the uncertainty, or offset concerns, then this will massively magnify the effects of AMOC," he said.

If governments behave as they have during past shocks, such as the 2008 financial crisis and the COVID-19 pandemic, they may restrict trade and start stockpiling food if and when the effects of AMOC weakening begin to show, Hinge predicted. The researchers hope their work and future modeling can help people understand the need to take swift, effective action if a collapse were to occur, he said.

Currently, "if that warning light starts flashing, we're not sure what to do with it," Hinge said. "There are no easy answers here; an AMOC [collapse] would be deeply catastrophic."

'> 'There are no easy answers here': New study forecasts a dire dip in food production if key Atlantic currents collapse
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