Chihuahuas have about 0.2 percent wolf ancestry, according to a new study. Breakthroughs, discoveries, and DIY tips sent every weekday. While that chihuahua might seem about as similar to a wolf as a shrub is to a mighty redwood tree, some small breeds like the tiny, big-eared chihuahua have some wolf ancestry. New research published today in the journal (), finds that the majority of dogs living today have low but detectable levels of post-domestication wolf ancestry. These genes have likely helped shape multiple characteristics, including personality traits, sense of smell, and body size.

Communication plays a vital role in human interaction. Studying language is a worthwhile task and more recently has become quantitative in nature with developments of fields like quantitative comparative linguistics and lexicostatistics. With respect to the authors own native languages, the ancestry of the English language and the Latin alphabet are of the primary interest. The Indo-European Tree traces many modern languages back to the Proto-Indo-European root. Swadesh's cognates played a large role in developing that historical perspective where some of the primary branches are Germanic, Celtic, Italic, and Balto-Slavic. This paper will use data analysis on open books where the simplest singular space is the 3-spider - a union T3 of three rays with their endpoints glued at a point 0 - which can represent these tree spaces for language clustering. These trees are built using a single linkage method for clustering based on distances between samples from languages which use the Latin Script. Taking three languages at a time, the barycenter is determined. Some initial results have found both non-sticky and sticky sample means. If the mean exhibits non-sticky properties, then one language may come from a different ancestor than the other two. If the mean is considered sticky, then the languages may share a common ancestor or all languages may have different ancestry.

Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals, underscoring a critical gap in genetic research. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data. We evaluate the performance of Group-LASSO INTERaction-NET (glinternet) and pretrained lasso in disease prediction focusing on diverse ancestries in the UK Biobank. Models were trained on data from White British and other ancestries and validated across a cohort of over 96,000 individuals for 8 diseases. Out of 96 models trained, we report 16 with statistically significant incremental predictive performance in terms of ROC-AUC scores (p-value < 0.05), found for diabetes, arthritis, gall stones, cystitis, asthma and osteoarthritis. For the interaction and pretrained models that outperformed the baseline, the PRS score was the primary driver behind prediction. Our findings indicate that both interaction terms and pre-training can enhance prediction accuracy but for a limited set of diseases and moderate improvements in accuracy.

It's something that many people are self-conscious of, and if you not a fan of your nose, we finally know who to blame. Scientists have revealed that Neanderthal DNA helps dictate the shape of your nose. A new study led by UCL researchers found that a particular gene, which leads to a taller nose, may have been the product of natural selection as ancient humans adapted to colder climates after leaving Africa. Dr Kaustubh Adhikari, who led the study, said: 'In the last 15 years, since the Neanderthal genome has been sequenced, we have been able to learn that our own ancestors apparently interbred with Neanderthals, leaving us with little bits of their DNA. 'Here, we find that some DNA inherited from Neanderthals influences the shape of our faces.

Finding out Taylor Swift was her 11th cousin twice-removed wasn't even the most shocking discovery Cher Scarlett made while exploring her family history. "There's a lot of stuff in my family that's weird and strange that we wouldn't know without Ancestry," says Scarlett, a software engineer and writer based in Kirkland, Washington. "I didn't even know who my mum's paternal grandparents were." In February 2022, the facial recognition search engine PimEyes surfaced non-consensual explicit photos of her at age 19, reigniting decades-old trauma. She attempted to get the pictures removed from the platform, which uses images scraped from the internet to create biometric "faceprints" of individuals.

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Medications like dextromethorphan, used to treat coughs caused by cold and flu, could potentially be repurposed to help people quit smoking cigarettes, according to a study by Penn State College of Medicine and University of Minnesota researchers. They developed a novel machine learning method, where computer programs analyze data sets for patterns and trends, to identify the drugs and said that some of them are already being tested in clinical trials. Cigarette smoking is risk factor for cardiovascular disease, cancer and respiratory diseases and accounts for nearly half a million deaths in the United States each year. While smoking behaviors can be learned and unlearned, genetics also plays a role in a person's risk for engaging in those behaviors. The researchers found in a prior study that people with certain genes are more likely to become addicted to tobacco.

Machine learning techniques, such as deep learning, have proven surprisingly effective at identifying diseases like breast cancer. However, when it comes to identifying mutations at the genetic level, these models have come up short, according to researchers at the University of California San Diego (UCSD). In a paper published in the journal Nature Biotechnology this week, researchers at the university propose a new machine learning framework called DeepMosaic that uses a combination of image-based visualization and deep learning models to identify genetic mutations associated with diseases including cancer and disorders with genetic links, such as autism spectrum disorder. Using AI/ML to identify disease has been a hot topic in recent years. The problem, according to UCSD professor Joe Gleeson, is most of these models aren't well suited to identifying genetic mutations, called mosaic variants or mutations, because most of the software developed over the last two decades was trained on cancer samples. Because cancer cells divide so rapidly, they're relatively easy to spot for computer programs, he explained in an interview with The Register.

The season of holiday gift buying is upon us, and it can be hard to resist the coolest new tech gadgets. But not all items are created equal when it comes to privacy, experts say. In the US, there are few limits on what companies can do with your data, putting the onus on us to do our homework, says Hayley Tsukayama, a senior legislative activist at the digital advocacy group the Electronic Frontier Foundation. She urges people to think through the privacy implications of gifts they're giving to friends and family. "Think about what information is going to be collected," she said.

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