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To combat fraudulent sales of low-aged ginseng disguised as high-aged ginseng, scientists from Shanghai University of Traditional Chinese Medicine in China, developed machine learning (ML) models to predict the ages of ginseng samples. Their work was published in the Journal of Separation Science (1).

The study aims to differentiate mountain-cultivated ginseng by age. Ginseng has been studied for its multiple health benefits, including boosting the immune system and fighting conditions like colds, flu, and cancer. Mountain-cultivated ginseng is usually harvested after 10 years and can produce more berries and seeds than wild ginseng plants, or ginseng plants harvested after 15 years. This difference has led to issues in the ginseng market, with some fraudulently selling low-aged cultivated ginseng and disguising it as high-aged.

For this experiment, 98 ginseng samples were analyzed using liquid chromatographymass spectrometry (LCMS), with multivariate statistical analysis used to identify patterns between samples and influential components. ML models were also created to assist in this process. Untargeted metabolomic analysis divided samples that were between 420 years into three age groups, with 22 age-dependent biomarkers discovered to differentiate between said age groups.

From there, three more ML models were made to predict new samples, which eventually led to an optimal model being selected. According to the scientists, Some biomarkers could determine age phases according to the differentiation of mountain-cultivated ginseng samples (1). The biomarkers were later analyzed for potential variation trends.

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Machine Learning Models Help Researchers Predict the Ages of ... - LCGC Chromatography Online

Last fall, the artist and musician Holly Herndon visited Torreciudad, a shrine to the Virgin Mary associated with the controversial Catholic group Opus Dei, in Aragn, Spain. The sanctuary, built in the nineteen-seventies, sits on a cliff overlooking an inviting blue reservoir, in a remote area just south of the Pyrenees. Herndon and her husband, Mathew Dryhurst, had been on a short vacation in the mountains nearby. They were particularly taken with an exhibit of Virgin Mary iconography from around the world: a faceless, abstract stone carving from Cameroon; a pale, blue-eyed statuette from Ecuador; a Black Mary from Senegal, dressed in an ornate gown of blue and gold. Moving from art work to art work, the couple discussed Marys embedding. In machine learning, embeddings distill data down to concepts. They are what enable generative A.I. systems to process prompts such as Cubist painting of a tabby cat, wearing a hot-dog costume and eating a hot dog or country-club application, as a sestina. At Torreciudad, the sculptures and paintings on display all had aesthetic and material differences, yet there was something consistentineffable but essentialthat made the art works legible depictions of the same figure.

Around this time, Herndon and Dryhurst, who is also her primary collaborator, had been experimenting with the embedding of Holly Herndon in the data used to train text-to-image generators such as Dall-E and Stable Diffusion. Herndon, who is forty-three, has sea-glass-blue eyes, a round, pale face, and persimmon-colored hair; she tends to style it with bangs, a short bob in front, and a long braid in the back. The embedding of the Virgin Mary might be reduced to something involving her posture, gaze, and infant son; Herndons embedding is tied to her distinctive look. In 2021, she and Dryhurst began working on a series of computer-generated images, grouped under the title CLASSIFIED, that explored her embedding in an artificial neural network created by OpenAI. Though some of the art works are unsettling portraits of Herndonesque women rendered in the style of an oil painting, many are more playful: x|o40, which used the prompt A building that looks like Holly Herndon, shows a stately white structure with brick-red bangs, two porthole windows, and pursed pink lips; x | o 41 depicts a figure with buggy blue eyes and a red braid which could be fan art for The Simpsons. My identity in models is determined by aggregate cliches scraped from the web, Herndon recently tweeted. Im mostly a haircut!

Herndon is perhaps best known for her experimental electronic music, and for an art practice that spans the art world, academia, and the tech industry. She has performed and shown work at the Guggenheim, the Pompidou, and the Kunstverein in Hamburg; next year, she and Dryhurst have an exhibition at the Serpentine, in London, and will be part of a prestigious group show this spring in New York. (When asked if the group show was the kind that happened only biennially, Herndon declined to elaborate.) In recent years, she and Dryhurst have also fought for artists self-determination in the era of A.I. I always felt they were so far ahead of everybody else, Hans Ulrich Obrist, the artistic director of the Serpentine, said. They really think about what it does to the whole ecosystem: the artistic, the technical, the social, the economic aspects of these technologies.

Since 2020, Herndon and Dryhurst have been refining Holly+, a machine-learning model trained on Herndons voice. They refer to the model as a digital twin and a vocal deepfake, and see it as an experiment in decentralizing control of Herndons public identity. Ive never really fetishized my voice, Herndon told me. I always thought my voice was an input, like a signal input into a laptop. Holly+ can use a timbre-transfer machine-learning model to translate any audio filea chorus, a tuba, a screeching traininto Herndons voice. It can also be used in real time or be fed a score and lyrics: last year, Herndon gave a TED talk that opened with a recording of Holly+ singing an arrangement by Maria Arnal, a Catalan musician. It was a performance Herndon could never do. These beautiful, melismatic runsyou have to study that stuff for years, she said. (She also does not speak Catalan.) Several months later, Herndon released a track in which Holly+ covers Jolene, by Dolly Parton. Its glitchy, with oddly placed breaths and slurred phrases, and is weirdly compelling. A free version of Holly+ is available online. When I uploaded a clip of sea lions barking, it returned a grunting, stuttering, portentous motet.

Holly+ represents the future that Herndon and Dryhurst anticipate for music, art, and literature: a world of infinite media, in which anyone can adjust, adapt, or iterate on the work, talents, and traits of others. The two refer to the process of generating new media this way as spawningan act they distinguish from well-known forms of allusion such as sampling, pastiche, collage, and homage. When a d.j. samples an audio clip from another artist, the clip is copied, then recontextualized. Neural networks, on the other hand, dont reproduce their training data but represent its internal logicsomething like a style, a mood, or a vibe. Herndon uses the phrase identity playa pun of sorts on I.P.to describe the act of allowing other people to use her voice. What if people were performing through me, on tour? she said. Kind of like body swapping, or identity swapping. I think that sounds exciting. Decisions about what to do with Holly+ are made by a decentralized autonomous organizationa sort of coperative group of digital stewards. (Herndon retains a veto.) The musician Caroline Polachek told me, I see it as an inevitability that voice modelling will be outside of artists control, that people will eventually be able to use my voice with or without my consent. Holly specifically has woken up a lot of the art and music community to this window of time we have, to determine what we want to do with that.

Essentially a dress shoe, but you could run for your life in them.

Cartoon by Edward Frascino

In conversation, Dryhurst described Holly+ as an abstracted fork of Herndons identityin open-source-software development, forking is the act of copying source code and then changing it. Herndon alternated between calling it my voice and the voice. Its not like you dont have a relationship with that version of you, she told me. Its still an emotional connection, but its not you. Public identities already take on lives of their own, the couple noted; most of the publicly available images of Herndon, which CLASSIFIED drew from, are press photos. Years ago, while experimenting with machine-learning software, she and Dryhurst realized that all existing media could be used to train A.I. systems, an idea that now informs their art practice. As soon as something is machine-legible, its part of a training canon, Herndon told me. And thats very radicalizing.

We were sitting outside their bedroom in Berlin, in a white-walled apartment so spacious, high-ceilinged, and affordable that it felt almost like a slight. Their infant son, Link, played quietly with a babysitter in the living room. A large print by the artist Trevor Paglen, titled Tornado (Corpus: Spheres of Hell) Adversarially Evolved Hallucination, hung over the couch; it depicted a neural networks concept of a tornado. In the bedroom, previously Herndons music studio, large white acoustic panels hung from the walls and ceiling, framing a low, unmade bed and a small bookcaseMark Fisher, Michel Houellebecq, Baby-Led Weaning. A towering dieffenbachia plant, inherited from an elderly neighbor who had recently died, slouched against the doorframe. Dryhurst, who is thirty-nine, bald, and bespectacled, offered to demonstrate Holly+. See if it sounds the same with speech, Herndon, who was wearing white overalls, instructed. Dryhurst picked up a microphone, and chatted for a moment; Holly+, processing his voicehe has an English accentsounded drunk and a little congested. Its optimized for singing, Herndon said, laughing. Dryhurst sang a sequence of notes. After a tiny lag, Holly+ began to harmonize with him, and then the real Herndon joined in. The choral effect was pleasant, if chaotic. Shes definitely a better singer than I am, Herndon said.

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Holly Herndon's Infinite Art - The New Yorker

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A novel method for identifying key genes in macroevolution based ... - Nature.com

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Development of predictive models for lymphedema by using blood ... - Nature.com

MADISON, Wis.University of Wisconsin-Madison researchers said they were proud to publish a groundbreaking paper on a new MRI machine-learning network.

They determined how brightly colored scans can help surgeons recognize, and accurately remove, an intracerebral hemorrhage (ICH), or bleeding in the brain.

Walter Block, a professor of medical physics and biomedical engineering, leads the research team that developed a special algorithm to support doctors who must act quickly and with precision to extract a brain bleed.

The trick is to visualize it and quantify it so that the surgeon has the information they need, Block said.

Tom Lilieholm a PhD candidate and lead author of the research created the specific algorithm for the new color-coded MRI machine-learning network.

We got pretty high accurate segmentations out of the machine here, 96% accurate clot, 81% accurate edema, he said, showing off one of the studys MRI slides.

Lilieholm said it can show a surgeon in less than a minute just how much of the hemorrhage they can safely remove.

Its really kind of useful to have that, and to have robust data to compare against, Lilieholm said. Thats where Matt kind of came in.

The Matt Lilieholm was referring to is NFL player Matt Henningsen.

Henningsen is from Menomonee Falls. Before becoming a Denver Bronco, he attended UW-Madison, where he excelled on the football field and in the classroom. He earned a bachelors and masters degree from the university.

My task would be to identify the location of the intracerebral hemorrhage and segment both the clot and the edema surrounding the clot, and then move on to every single layer of that image, Henningsen said.

Henningsen spent more than 100 hours gathering data for this new research on brain bleeds. He said he was excited and grateful for the opportunity to be part of this collaboration.

The UW-trained bioengineer and football player said he hopes this project can eventually support and improve something his football profession fears: traumatic brain injury.

You cant diagnose concussion with an MRI currently, he said. But I mean, maybe in the future, if youre able to, you can use machine-learning to potentially detect certain abnormalities that the human eye couldnt necessarily detect or things of that sort. Maybe we could get somewhere.

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UW scientists and NFL player create new MRI machine-learning ... - Spectrum News 1