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Intel quietly launched mysterious new AI CPU that promises to bring deep learning inference and computing to the edge — but you won’t be able to plug them in a motherboard anytime soon

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Intel has launched a new AI processor series for the edge, promising industrial-class deep learning inference. The new ‘Amston Lake’ Atom x7000RE chips offer up to double the cores and twice the higher graphics base frequency as the previous x6000RE series, all neatly packed within a 6W–12W BGA package.

The x7000RE series packs more performance into a smaller footprint. Boasting up to eight E-cores it supports LPDDR5/DDR5/DDR4 memory and up to nine PCIe 3.0 lanes, delivering robust multitasking capabilities.

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DNA from ancient graves reveals the culture of a mysterious nomadic people

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Excavation works conducted by the Eötvös Loránd University at the Avar-period (6th-9th century AD) cemetery of Rákóczifalva, Hungary, in 2006.

Scientists sampled genomic data from 279 graves at a cemetery in Rákóczifalva, Hungary, where people of the medieval Avar culture were buried.Credit: Institute of Archaeological Sciences, Eötvös Loránd University Múzeum, Budapest, Hungary

Most people know about the Huns, if only because of their infamous warrior-ruler Attila. But the Avars, another nomadic people who subsequently occupied roughly the same region of eastern and central Europe, have remained obscure despite having assembled a sprawling empire that lasted from the late sixth century to the early ninth century. Even archaeologists have struggled to piece together their history and culture, relying on spotty and potentially biased contemporaneous chronicles that, in many cases, were authored by the Avars’ adversaries.

A deep dive into 424 genomes collected from hundreds of Avar graves is filling in crucial gaps in this story, revealing a wealth of insights into the Avars’s social structure and culture1. “These people basically didn’t have a voice in history, and we are kind of looking into them this way — through their bodies,” says Zuzana Hofmanová, an archaeogeneticist at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, and one of the study’s lead authors.

The work was published today in Nature.

Nine generations

The researchers focused on four cemeteries in Hungary that were once at the heart of the khaganate, as the former Avar empire was known. Importantly, all four sites were fully excavated, giving the researchers access to DNA from every grave and enabling them to use genetic data to map relatedness for entire Avar communities.

This effort got an important boost from a computational method called ancIBD, which can connect even distant family members on the basis of their shared chromosomal sequences2. Co-lead author Johannes Krause, an archaeogeneticist at Max Planck, says that scientists have generally struggled to reassemble DNA-based family trees that extend past third-degree relatives, such as first cousins or great-grandparents. But by using tools such as ancIBD, Krause and colleagues were able to chart much more convoluted Avar family trees, including a massive nine-generation pedigree comprising 146 family members.

The data suggest that, after migrating to Europe, the Avars retained many cultural practices from their place of origin on the northeast Asian steppes3. For example, the Avars were very strict about avoiding inbreeding. There were no observed instances of marriage between relatives — even at the level of second cousins. Krasue says that was surprising, given that unions between first cousins were not unusual during much of European history. “It’s really remarkable that they can keep track over nine generations who is related to whom, and who can have children with whom,” he says.

On the other hand, there was also limited intermarriage with non-Avar neighbors: about 20% of the genomic sequences in the sampled Avar DNA could be traced to central European ancestry.

Gold figurine from the excavation at Rákóczifalva, Hungary.

A gold figurine excavated from an Avar burial site in Rákóczifalva, Hungary.Credit: Institute of Archaeological Sciences, Eötvös Loránd University Múzeum, Budapest, Hungary

The researchers recorded several examples of ‘levirate unions’, in which a widow married a male from the family of her deceased spouse, such as a brother. Such marital patterns were atypical in much of Europe, but were established features of Asian steppe-dwelling cultures, notes co-lead author Tivadar Vida, an archaeologist at Eötvös Loránd University in Budapest. “It was archaeologically very interesting to see the conservativism in the Avar society, lasting nine generations,” says Vida.

The Avars were also strictly patrilineal, with men acting as heads of family and daughters leaving their communities to join their husbands’ households. At the largest cemetery sampled, in the village of Rákóczifalva, Hungary, Hofmanová notes that there was only a single instance of both a mother and her adult daughter being interred.

Power play

The kinship data reveal what seems to be a shift in local political power that would have been difficult to detect with sparse DNA sampling. In the graves at Rákóczifalva, the researchers found that one male lineage predominated early in Avar history, but was displaced by a different Avar bloodline by the late seventh century. Intriguingly, archaeological evidence collected from those graves suggests that the subsequent family had different diets and burial rituals than did the displaced one, indicating that Avar culture shifted over time despite relatively modest levels of intermarriage with non-Avar individuals.

Carles Lalueza-Fox, a palaeogenomicist at the Institute of Evolutionary Biology in Barcelona, Spain, says that this work demonstrates the richness of the insights that can emerge when researchers have the opportunity and resources to broadly survey and analyse DNA at sites of historical interest. “Only this scale of analysis would allow you to obtain a reliable picture of kinship and social processes,” he says, adding that his group is now embracing a similar approach in their archaeogenomic research. “I think ancient genomics is moving toward this direction to obtain a more democratic and nuanced view of the past.”

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Beats tipped to launch a new Bluetooth speaker after LeBron James flaunts mysterious device

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US basketball star LeBron James has long been part of Beats’ history, during which time he’s also been known to leak upcoming products – and it looks like he may have  done it again. The NBA legend was captured with what appears to be a brand new and unreleased Bluetooth speaker in a reel posted to Instagram (see below) by the LA Lakers. In it, you can clearly see the Pill-shaped speaker has a Beats logo – and it’s on a lanyard, which is something the most recent Beats Pill speaker didn’t have.

The Beats Pill Plus was killed off in January 2022, but it wouldn’t be up for consideration as one of the best Bluetooth speakers if it were still being sold today. It dated back to Apple‘s acquisition of Beats – it was the first Beats speaker to come with a Lightning port, which was Apple’s connector of choice at the time – and was considered by many to be overpriced at launch in 2015, never mind years later when Apple finally stopped selling it.

What we’d like to see in a new Beats Pill speaker



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AI traces mysterious metastatic cancers to their source

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Coloured scanning electron micrograph of a cultured breast cancer cell (orange) moving through two holes in a support film.

A breast cancer cell (artificially coloured) climbs through a supportive film in a laboratory experiment.Credit: Steve Gschmeissner/SPL

Some stealthy cancers remain undetected until they have spread from their source to distant organs. Now scientists have developed an artificial intelligence (AI) tool that outperforms pathologists at identifying the origins of metastatic cancer cells that circulate in the body1. The proof-of-concept model could help doctors to improve the diagnosis and treatment of late-stage cancer, and extend people’s lives.

“That’s a pretty significant finding — that it can be used as an assistive tool,” says Faisal Mahmood, who studies AI applications in health care at Harvard Medical School in Boston, Massachusetts.

Elusive origins

To treat metastatic cancers, doctors need to know where they came from. The origin of up to 5% of all tumours cannot be identified, and the prognosis for people whose primary cancer remains unknown is poor.

One method used to diagnose tricky metastatic cancers relies on tumour cells found in fluid extracted from the body. Clinicians examine images of the cells to work out which type of cancer cell they resemble. For example, breast cancer cells that migrate to the lungs still look like breast cancer cells.

Every year, of the 300,000 people with cancer who are newly treated at the hospital affiliated with Tianjin Medical University (TMU) in China, some 4,000 are diagnosed using such images, but around 300 people remain undiagnosed, says Tian Fei, a colorectal cancer surgeon at TMU.

Tian, Li Xiangchun, a bioinformatics researcher who studies deep learning at TMU, and their colleagues wanted to develop a deep-learning algorithm to analyse these images and predict the origin of the cancers. Their results were published in Nature Medicine on 16 April.

Tumour training

The researchers trained their AI model on some 30,000 images of cells found in abdominal or lung fluid from 21,000 people whose tumour of origin was known. They then tested their model on 27,000 images and found there was an 83% change that it would accurately predict the source of the tumour. And there was a 99% chance that the source of the tumour was included in the model’s top three predictions.

Having a top-three list is useful because it can help clinicians to reduce the number of extra — often intrusive — tests needed to identify a tumour’s origins, says Mahmood. The predictions were restricted to 12 common sources of cancer, including the lungs, ovaries, breasts and stomach. Some other forms of cancer, including those originating in the prostate and kidneys, could not be identified, because they don’t typically spread to fluid deposits in the abdomen and lungs, says Li.

When tested on some 500 images, the model was better than human pathologists at predicting a tumour’s origin. This improvement was statistically significant.

The researchers also retrospectively assessed a subset of 391 study participants some four years after they had had cancer treatment. They found that those who had received treatment for the type of cancer that the model predicted were more likely to have survived, and lived longer, than participants for whom the prediction did not match. “This is a pretty convincing argument” for using the AI model in a clinical setting, says Mahmood.

Mahmood has previously used AI to predict the origin of cancers from tissue samples2, and other teams have used genomic data. Combining the three data sources — cells, tissue and genomics — could further improve outcomes for people with metastatic cancers of unknown origins, he says.

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