PillBot’s thrusters and high-res cameras make remote stomach diagnostics a reality—revolutionizing gastroenterology.
Endiatx’s swallowable camera uses pumpjet thrusters for remote stomach exams, replacing invasive procedures and advancing telemedicine.
Anyone who has operated a 3D printer before, especially those new to using these specialized tools, has likely had problems with the print bed. The bed might not always be the correct temperature leading to problems with adhesion of the print, it could be uncalibrated or dirty or cause any number of other issues that ultimately lead to a failed print. Most of us work these problems out through trial and error and eventually get settled in, but this novel 3D printer instead removes the bed itself and prints on whatever surface happens to be nearby.
The printer is the product of [Daniel Campos Zamora] at the University of Washington and is called MobiPrint. It uses a fairly standard, commercially available 3D printer head but attaches it to the base of a modified robotic vacuum cleaner. The vacuum cleaner is modified with open-source software that allows it to map its environment without the need for the manufacturer’s cloud services, which in turn lets the 3D printer print on whichever surface the robot finds in its travels. The goal isn’t necessarily to eliminate printer bed problems; a robot with this capability could have many more applications in the realm of accessibility or even, in the future, printing while on the move.
There were a few surprising discoveries along the way which were mentioned in an IEEE Spectrum article, as [Campos Zamora] found while testing various household surfaces that carpet is surprisingly good at adhering to these prints and almost can’t be unstuck from the prints made on it. There are a few other 3D printers out there that we’ve seen that are incredibly mobile, but none that allow interacting with their environment in quite this way.
Originally published on Towards AI.
In its most basic form, Bayesian Inference is just a technique for summarizing statistical inference which states how likely an hypothesis is given any new evidence. The method comes from Bayes’ Theorem, which provides a way to calculate the probability that an event will happen or has happened, given any prior knowledge of conditions (from which an event may not happen):
Here’s a somewhat rough rendering of Bayes’ Theorem:
Assuming dark matter exists, its interactions with ordinary matter are so subtle that even the most sensitive instruments cannot detect them. In a new study, Northwestern University physicists now introduce a highly sensitive new tool, which amplifies incredibly faint signals by 1,000 times—a 50-fold improvement over what was previously possible.
Called an atom interferometer, the incredibly precise tool manipulates atoms with light to measure exceptionally tiny forces. But, unlike other atom interferometers, which are limited by the imperfections in the light itself, the new tool self-corrects for these imperfections to reach record-breaking levels of precision.
By boosting imperceptible signals to perceptible levels, the technological advance could help scientists who are hunting for ultra-weak forces emitted from a variety of evasive phenomena, including dark matter, dark energy and gravitational waves in unexplored frequency ranges.
A quantum experiment revealed two observers can experience different, coexisting realities.
Our understanding of reality is often shaped by biases—our senses, cultures, and knowledge influence how we see the world. But even science, often regarded as a path to objective truth, may not always offer a single, consistent version of reality. A recent experiment testing a 1961 thought experiment by Nobel Prize winner Eugen Wigner highlights this issue, showing that two versions of reality can coexist in the quantum world.
Published in Nature Communications, a new study led by the University of Minnesota Medical School and Duke University found that a DNA sequencing test for advanced prostate cancer patients can distinguish between patients with poor and favorable prognoses.
The new blood-based test —called AR-ctDETECT—is designed to detect and analyze small fragments of tumor-derived DNA in the blood of certain patients with advanced, metastatic prostate cancer.
In this new study, the AR-ctDETECT test was used to analyze DNA from more than 770 blood samples from a phase 3 clinical trial of advanced prostate cancer patients. The test identified circulating tumor DNA (ctDNA) in 59% of patients with metastatic prostate cancer. Patients with detectable circulating tumor DNA had significantly worse overall survival compared to those without. These results demonstrate the potential of the AR-ctDETECT test to provide key genetic information to tailor treatments based on similar characteristics among patients.
New research found that the protein MANF helps cells manage toxic protein clumps, improving cellular health and potentially aiding treatments for age-related diseases like Alzheimer’s and Parkinson’s.
Researchers at McMaster University have uncovered a previously unidentified cell-protective role of a protein, potentially paving the way for new treatments for age-related diseases and promoting healthier aging.
The team has found that a class of protective proteins known as MANF plays a role in the process that keep cells efficient and working well.
A newly published research study from the UNC Lineberger Comprehensive Cancer Center describes how the absence of the protein NLRP12 significantly increases susceptibility to colitis-associated colon cancer in pre-clinical models.
A family of proteins is yielding new information about how it contributes to the development of gastrointestinal disease and cancer. A team of UNC scientists reports that in pre-clinical models, the absence of a protein called NLRP12 significantly increases susceptibility to colitis-associated colon cancer.
The NLR family of proteins is very complex and scientists have determined that the majority of them act as activators of inflammation. However, scientists at UNC and elsewhere have recently reported that one NLR protein, NLRP12, actually functions to reduce disease by inhibiting a major inflammatory pathway mediated by a protein called NF-Kappa B activation has been long associated with inflammation and cancer promotion. But NF-Kappa B has an alternate signaling pathway that is not as well understood. This alternative pathway was the focus of the UNC team’s study. Their study was published in the April 12, 2012 online issue of the journal Immunity.
UNIVERSITY PARK, Pa. — A recently developed electronic tongue is capable of identifying differences in similar liquids, such as milk with varying water content; diverse products, including soda types and coffee blends; signs of spoilage in fruit juices; and instances of food safety concerns. The team, led by researchers at Penn State, also found that results were even more accurate when artificial intelligence (AI) used its own assessment parameters to interpret the data generated by the electronic tongue.
(Many people already posted this. This is the press release from Penn Sate who did the research)
The tongue comprises a graphene-based ion-sensitive field-effect transistor, or a conductive device that can detect chemical ions, linked to an artificial neural network, trained on various datasets. Critically, Das noted, the sensors are non-functionalized, meaning that one sensor can detect different types of chemicals, rather than having a specific sensor dedicated to each potential chemical. The researchers provided the neural network with 20 specific parameters to assess, all of which are related to how a sample liquid interacts with the sensor’s electrical properties. Based on these researcher-specified parameters, the AI could accurately detect samples — including watered-down milks, different types of sodas, blends of coffee and multiple fruit juices at several levels of freshness — and report on their content with greater than 80% accuracy in about a minute.
“After achieving a reasonable accuracy with human-selected parameters, we decided to let the neural network define its own figures of merit by providing it with the raw sensor data. We found that the neural network reached a near ideal inference accuracy of more than 95% when utilizing the machine-derived figures of merit rather than the ones provided by humans,” said co-author Andrew Pannone, a doctoral student in engineering science and mechanics advised by Das. “So, we used a method called Shapley additive explanations, which allows us to ask the neural network what it was thinking after it makes a decision.”
The new study demonstrates the ability to learn new skills without conscious effort.
Researchers have developed a groundbreaking technique using brain imaging and neurofeedback to induce learning without conscious effort.
