Lifeboat Foundation

With artificial intelligence poised to assist in profound scientific discoveries that will change the world, Cornell is leading a new $11.3 million center focused on human-AI collaboration that uses mathematics as a common language.

The Scientific Artificial Intelligence Center, or SciAI Center, is being launched with a grant from the Office of Naval Research and is led by Christopher J. Earls, professor of civil and environmental engineering at Cornell Engineering. Co-investigators include Nikolaos Bouklas, assistant professor of mechanical and aerospace engineering at Cornell Engineering; Anil Damle, assistant professor of computer science in the Cornell Ann S. Bowers College of Computing and Information Science; and Alex Townsend, associate professor of mathematics in the College of Arts and Sciences. All of the investigators are field faculty members of the Center for Applied Mathematics.

With the advance of AI systems – built with tangled webs of algorithms and trained on increasingly large sets of data – researchers fear AI’s inner workings will provide little insight into its uncanny ability to recognize patterns in data and make scientific predictions. Earls described it as a situation at odds with true scientific discovery.

A recent work introduces a cellular deconvolution method, MeDuSA, of estimating cell-state abundance along a one-dimensional trajectory from bulk RNA-seq data with fine resolution and high accuracy, enabling the characterization of cell-state transition in various biological processes.

Single-cell transcriptomic techniques continue to revolutionize the resolution of cell analysis, determining discrete cell types and cell states with continuous dynamic transitions that can be related to development and disease progression⁵. Cells in different states can be computationally ordered according to a pseudo-time series, or cell trajectory⁶. Both MeDuSA and another method, Cell Population Mapping (CPM)⁷, were developed to exploit the rich spectrum of single-cell reference profiles to estimate cell-state abundance in bulk RNA-seq data, which enables fine-resolution cellular deconvolution (Fig. 1b). Although CPM effectively tackles the issue of estimating the abundance of cells in different states, MeDuSA further improves the estimation accuracy by employing a LMM (see the equation in Fig. 1c) that takes into account both the cell state of interest (focal state) and the remaining cells of the same cell type (non-focal state) as well as the other cell types.

To show the capability of the OrganoidChip in enabling higher-resolution imaging, we used confocal microscopy for several organoids immobilized on the chip. Representative images show improved optical segmentation and the ability to resolve single cells within an organoid (Fig. 4 d). The co-localized EthD-1-and Hoechst-stained nuclei are resolvable and can potentially be used to increase the accuracy of viability measurements. Future implementation of 3D-segmentation using AI-assisted algorithms in the analysis pipeline can provide more accurate estimations of cellular viability in larger screens.

Next, we measured the effect of DOX treatment on the beating kinetics of cardiac organoids. To do this, we relied on calcium fluorescence imaging, as it has been shown to be a good approximation of the cardiomyocytes’ action potentials³². Calcium imaging proved beneficial for beating and contraction parameters since smaller beating portions cannot necessarily be detected from brightfield images, particularly when organoids have been compromised as a result of drug treatment.

When assessing drug effects, we observed some degree of variability in the spontaneous contractile behaviour and beating kinetics between cardiac organoids. Such variability often skews any averaged parameter value across organoids and does not reflect the effect of the treatment conditions on organoid health. To address this challenge, we tracked each individual organoid’s beating off-and on-chip. The drug-induced functionality results are therefore reported as averages of fractional changes of each individual organoid’s beating kinetics parameters, measured at 48 h post-treatment, on both the chamber slide and on the chip, relative to its pre-treatment value (Eq. 3).

An interdisciplinary team at the Advanced Quantum Testbed (AQT) at Lawrence Berkeley National Laboratory (Berkeley Lab) and the University of California, Berkeley’s Quantum Nanoelectronics Laboratory (QNL) achieved a technical breakthrough using qutrits—three-level systems—on a superconducting quantum processor.

The team successfully entangled two transmon qutrits with gate fidelities significantly higher than in previously reported works, thus getting closer to enabling ternary logic that can encode more information than their binary counterparts—qubits.

Published in Nature Communications in December 2022 and featured as an editor’s highlight, this experimental success pushes forward AQT’s qutrit research and development, including previous experimental successes published in 2021 in Physical Review X and Physical Review Letters. Ternary quantum information processors offer significant potential advantages in quantum simulation and error correction, as well as the ability to improve certain quantum algorithms and applications.

Apple’s Vision Pro headset will use a new type of dynamic random access memory, or DRAM, that has been custom designed to support Apple’s R1 input processing chip, reports The Korea Herald.

Apple Vision Pro is powered by a pair of chips. The main processor is the M2, which is responsible for processing content, running the visionOS operating system, executing computer vision algorithms, and providing graphical content.

Back in 1956, Denham Harman proposed that the aging is caused by the build up of oxidative damage to cells, and that this damage is caused by free radicals which have been produced during aerobic respiration [1]. Free radicals are u nstable atoms that have an unpaired electron, meaning a free radical is constantly on the look-out for an atom that has an electron it can pinch to fill the space. This makes them highly reactive, and when they steal atoms from your body’s cells, it is very damaging.

Longevity. Technology: As well as being generated in normal cell metabolism, free radicals can be acquired from external sources (pollution, cigarette smoke, radiation, medication, &c) and while the free radical theory of aging has been the subject of much debate [2], the understanding of the danger free radicals pose led to an increase in the public’s interest in superfoods, vitamins and minerals that were antioxidants – substances that have a spare electron which they are happy to give away to passing free radicals, thus removing them from the danger equation.

But before you reach for the blueberries, it is important to know that, as so often in biology, the story is not black and white. Like a misunderstood cartoon villain, free radicals have a beneficial side, too – albeit in moderation. Free radicals generated by the cell’s mitochondria are beneficial in wound-healing, and others elsewhere act as important signal substances. Used as weapons by the body’s defense system, free radicals destroy invading pathogenic microbes to prevent disease.

GitHub found that 92 percent of the 500 US-based developers they surveyed said that they integrate AI tools into their workflow.

Dr. ryan brinkman-vice president and research director, dotmatics

Scientists have long been perceived and portrayed in films as old people in white lab coats perched at a bench full of bubbling fluorescent liquids. The present-day reality is quite different. Scientists are increasingly data jockeys in hoodies sitting before monitors analyzing enormous amounts of data. Modern-day labs are more likely composed of sterile rows of robots doing the manual handling of materials, and lab notebooks are now electronic, in massive data centers holding vast quantities of information. Today, scientific input comes from data pulled from the cloud, with algorithms fueling scientific discovery the way Bunsen burners once did.

Advances in technology, and especially instrumentation, enable scientists to collect and process data at an unprecedented scale. As a result, scientists are now faced with massive datasets that require sophisticated analysis techniques and computational tools to extract meaningful insights. This also presents significant challenges—how do you store, manage, and share these large datasets, as well as ensure that the data is of high quality and reliable?

Artificial intelligence algorithms, such as the sophisticated natural language processor ChatGPT, are raising hopes, eyebrows and alarm bells in multiple industries. A deluge of news articles and opinion pieces, reflecting both concerns about and promises of the rapidly advancing field, often note AI’s potential to spread misinformation and replace human workers on a massive scale. According to Jonathan Chen, MD, PhD, assistant professor of medicine, the speculation about large-scale disruptions has a kernel of truth to it, but it misses another element when it comes to health care: AI will bring benefits to both patients and providers.

Chen discussed the challenges with and potential for AI in health care in a commentary published in JAMA on April 28. In this Q&A, he expands on how he sees AI integrating into health care.

The algorithms we’re seeing emerge have really popped open Pandora’s box and, ready or not, AI will substantially change the way physicians work and the way patients interact with clinical medicine. For example, we can tell our patients that they should not be using these tools for medical advice or self-diagnosis, but we know that thousands, if not millions, of people are already doing it — typing in symptoms and asking the models what might be ailing them.