Capsules embedded between layers of this fake tissue simulate human blood and pus. Small 3D-printed liquid capsules inserted between layers of tissue burst open, mimicking blood, when surgeons make an incision. Breakthroughs, discoveries, and DIY tips sent every weekday. Budding surgeons may soon train on stretchy, lifelike 3D-printed skin that oozes out blood and pus when cut. A new printable material developed by researchers at the University of Minnesota Twin Cities more closely mimics the adaptive nature of human tissue.

Music source separation demixes a piece of music into its individual sound sources (vocals, percussion, melodic instruments, etc.), a task with no simple mathematical solution. It requires deep learning methods involving training on large datasets of isolated music stems. The most commonly available datasets are made from commercial Western music, limiting the models' applications to non-Western genres like Carnatic music. Carnatic music is a live tradition, with the available multi-track recordings containing overlapping sounds and bleeds between the sources. This poses a challenge to commercially available source separation models like Spleeter and Hybrid Demucs. In this work, we introduce 'Sanidha', the first open-source novel dataset for Carnatic music, offering studio-quality, multi-track recordings with minimal to no overlap or bleed. Along with the audio files, we provide high-definition videos of the artists' performances. Additionally, we fine-tuned Spleeter, one of the most commonly used source separation models, on our dataset and observed improved SDR performance compared to fine-tuning on a pre-existing Carnatic multi-track dataset. The outputs of the fine-tuned model with 'Sanidha' are evaluated through a listening study.

Pediatric HAL is a well-known medical robot that really bleeds, cries, urinates, and mimics further Human behavior very well. Medical college students use HAL to learn how to diagnose and deal with illness earlier than operating with actual patients. Pediatric HAL is a part of a line of robots from a company known as Gaumard. Gaumard additionally makes robots that simulate pregnant people, newborns, and trauma wounds. Heading into 2019, buyers are being plagued with the aid of using a laundry listing of concerns.

To help it's nursing students get more real-world experience with pediatric patients, Bunker Hill Community College in Boston is turning to robots. They've been using Pediatric Hal to simulate a 5-year-old male patient. The robot comes from Miami based Gaumard Scientific who are known for their wide variety of medical simulators. Bunker Hill turned to robots partially due to the limited availability of clinical placement slots with patients in the area. Hal is as close to a real as you can get.

Monitoring physiological responses to hemodynamic stress can help in determining appropriate treatment and ensuring good patient outcomes. Physicians' intuition suggests that the human body has a number of physiological response patterns to hemorrhage which escalate as blood loss continues, however the exact etiology and phenotypes of such responses are not well known or understood only at a coarse level. Although previous research has shown that machine learning models can perform well in hemorrhage detection and survival prediction, it is unclear whether machine learning could help to identify and characterize the underlying physiological responses in raw vital sign data. We approach this problem by first transforming the high-dimensional vital sign time series into a tractable, lower-dimensional latent space using a dilated, causal convolutional encoder model trained purely unsupervised. Second, we identify informative clusters in the embeddings. By analyzing the clusters of latent embeddings and visualizing them over time, we hypothesize that the clusters correspond to the physiological response patterns that match physicians' intuition. Furthermore, we attempt to evaluate the latent embeddings using a variety of methods, such as predicting the cluster labels using explainable features.

A few years ago, on a warm sunny afternoon, my ninety-year-old father-in-law was sweeping his patio when he suddenly felt weak and dizzy. Falling to his knees, he crawled inside his condo and onto the couch. He was shaking but not confused when my wife, Susan, came over minutes later, since we lived just a block away. She texted me at work, where I was just finishing my clinic, and asked me to come over. When I got there, he was weak and couldn't stand up on his own, and it was unclear what had caused this spell.

Imagine it's the early 1990s and you've heard about a new platform called "the internet." You realize its potential for customer communication, so you want to build a web presence. You want to create a site that reflects your brand standards and communicates with customers in your brand's voice. That's exactly what some brands are doing today with AI assistants. Sales of voice-driven AI assistants like Amazon's Echo and Google Home are booming, and it's increasingly common for consumers to ask Alexa or Google about products or services they're interested in instead of entering keywords into a search engine.

Technology has come a long way when it comes to how we connect with people. And now at Geisinger, it's helping patients who need care first be treated first. Intracranial hemorrhaging, or a brain bleed, is a fairly common problem, according to Dr. Brandon Fornwalt, from the Department of Imaging Science & Innovation at Geisinger Medical Center near Danville. Scans show the brain of an elderly patient whose only symptom was confusion. "Lots of things can cause elderly patients to be confused. It's a diagnostic dilemma and it's an acute finding that needs to be treated very quickly," said Dr. Fornwalt.

Asimove's first law of robotics has been broken, writes an anonymous reader, sharing this article from Fast Company: A Berkeley, California man wants to start a robust conversation among ethicists, philosophers, lawyers, and others about where technology is going -- and what dangers robots will present humanity in the future. Alexander Reben, a roboticist and artist, has built a tabletop robot whose sole mechanical purpose is to hurt people… The harm caused by Reben's robot is nothing more than a pinprick, albeit one delivered at high speed, causing the maximum amount of pain a small needle can inflict on a fingertip. Though the pinpricks are delivered randomly, "[O]nce something exists in the world, you have to confront it. It becomes more urgent," says the robot's creator. "You can't just pontificate about it…. " But the article raises an interesting question.

Remember the old saying, "don't bite the hand that feeds you?" Well, this robot is choosing to prick the fingers that build it. "The First Law," as the robot is nicknamed, is a simple machine made up of sensors, a metal arm, and replaceable diabetes needles. A random algorithm programmed into the robot decides whether or not to deliver a sharp prick to your finger. The bot's creator, artist and roboticist, Alexander Reben, is not a masochist.