David Furman, an immunologist and data scientist at the Buck Institute for Research on Aging and Stanford University, uses artificial intelligence to parse big data to identify interventions for healthy aging.
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David Furman uses computational power, collaborations, and cosmic inspiration to tease apart the role of the immune system in aging.
“ tabindex=”0” KAIST researchers have discovered a molecular switch that can revert cancer cells back to normal by capturing the critical transition state before full cancer development. Using a computational gene network model based on single-cell RNA
Ribonucleic acid (RNA) is a polymeric molecule similar to DNA that is essential in various biological roles in coding, decoding, regulation and expression of genes. Both are nucleic acids, but unlike DNA, RNA is single-stranded. An RNA strand has a backbone made of alternating sugar (ribose) and phosphate groups. Attached to each sugar is one of four bases—adenine (A), uracil (U), cytosine ©, or guanine (G). Different types of RNA exist in the cell: messenger RNA (mRNA), ribosomal RNA (rRNA), and transfer RNA (tRNA).
In a milestone that brings quantum computing tangibly closer to large-scale practical use, scientists at Oxford University’s Department of Physics have demonstrated the first instance of distributed quantum computing. Using a photonic network interface, they successfully linked two separate quantum processors to form a single, fully connected quantum computer, paving the way to tackling computational challenges previously out of reach. The results have been published in Nature.
Scattering takes place across the universe at large and miniscule scales. Billiard balls clank off each other in bars, the nuclei of atoms collide to power the stars and create heavy elements, and even sound waves deviate from their original trajectory when they hit particles in the air.
Understanding such scattering can lead to discoveries about the forces that govern the universe. In a recent publication in Physical Review C, researchers from Lawrence Livermore National Laboratory (LLNL), the InQubator for Quantum Simulations and the University of Trento developed an algorithm for a quantum computer that accurately simulates scattering.
“Scattering experiments help us probe fundamental particles and their interactions,” said LLNL scientist Sofia Quaglioni. “The scattering of particles in matter [materials, atoms, molecules, nuclei] helps us understand how that matter is organized at a microscopic level.”
To identify signs of particles like the Higgs boson, CERN researchers work with mountains of data generated by LHC collisions.
Hunting for evidence of an object whose behavior is predicted by existing theories is one thing. But having successfully observed the elusive boson, identifying new and unexpected particles and interactions is an entirely different matter.
To speed up their analysis, physicists feed data from the billions of collisions that occur in LHC experiments into machine learning algorithms. These models are then trained to identify anomalous patterns.
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Human brain organoids (“mini-brains”) are being grown in labs around the world. They’re being fed neurotransmitters, competing with AI to solve non-linear equations, and going to space to study the effects of microgravity. This video reviews three preprints, preliminary reports of new scientific studies. (My AI voice caught a cold this week.)
Support the channel: https://www.patreon.com/ihmcurious.
Preprints:
- Brain Organoid Computing for Artificial Intelligence (Cai et al.) https://www.biorxiv.org/content/10.1101/2023.02.28.530502v1.full.
- Modulation of neuronal activity in cortical organoids with bioelectronic delivery of ions and neurotransmitters (Park et al.) https://www.biorxiv.org/content/10.1101/2023.06.10.544416v1.full.
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Our memristor is inspired and supported by a comprehensive theory directly derived from the underlying physical equations of diffusive and electric continuum ion transport. We experimentally quantitatively verified the predictions of our theory on multiple occasions, among which the specific and surprising prediction that the memory retention time of the channel depends on the channel diffusion time, despite the channel being constantly voltage-driven. The theory exclusively relies on physical parameters, such as channel dimensions and ion concentrations, and enabled streamlined experimentation by pinpointing the relevant signal timescales, signal voltages, and suitable reservoir computing protocol. Additionally, we identify an inhomogeneous charge density as the key ingredient for iontronic channels to exhibit current rectification (provided they are well described by slab-averaged PNP equations). Consequently, our theory paves the way for targeted advancements in iontronic circuits and facilitates efficient exploration of their diverse applications.
For future prospects, a next step is the integration of multiple devices, where the flexible fabrication methods do offer a clear path toward circuits that couple multiple channels. Additionally, optimizing the device to exhibit strong conductance modulation for lower voltages would be of interest to bring electric potentials found in nature into the scope of possible inputs and reduce the energy consumption for conductance modulation. From a theoretical perspective, the understanding of the (origin of the) inhomogeneous space charge and the surface conductance is still somewhat limited. These contain (physical) parameters that are now partially chosen from a reasonable physical regime to yield good agreement, but do not directly follow from underlying physical equations. We also assume that the inhomogeneous ionic space charge distribution is constant, while it might well be voltage-dependent.
A Canadian startup called Xanadu has built a new quantum computer it says can be easily scaled up to achieve the computational power needed to tackle scientific challenges ranging from drug discovery to more energy-efficient machine learning.
Aurora is a “photonic” quantum computer, which means it crunches numbers using photonic qubits—information encoded in light. In practice, this means combining and recombining laser beams on multiple chips using lenses, fibers, and other optics according to an algorithm. Xanadu’s computer is designed in such a way that the answer to an algorithm it executes corresponds to the final number of photons in each laser beam. This approach differs from one used by Google and IBM, which involves encoding information in properties of superconducting circuits.
Question Can an electrocardiography (ECG)–based artificial intelligence risk estimator for hypertension (AIRE-HTN) predict incident hypertension and stratify risk for incident hypertension-associated adverse events?
Findings In this prognostic study including an ECG algorithm trained on 189 539 patients at Beth Israel Deaconess Medical Center and externally validated on 65 610 patients from UK Biobank, AIRE-HTN predicted incident hypertension and stratified risk for cardiovascular death, heart failure, myocardial infarction, ischemic stroke, and chronic kidney disease.
Meaning Results suggest that AIRE-HTN can predict the development of hypertension and may identify at-risk patients for enhanced surveillance.
Optical fibers are fundamental components in modern science and technology due to their inherent advantages, providing an efficient and secure medium for applications such as internet communication and big data transmission. Compared with single-mode fibers (SMFs), multimode fibers (MMFs) can support a much larger number of guided modes (~103 to ~104), offering the attractive advantage of high-capacity information and image transportation within the diameter of a hair. This capability has positioned MMFs as a critical tool in fields such as quantum information and micro-endoscopy.
However, MMFs pose a significant challenge: their highly scattering nature introduces severe modal dispersion during transmission, which significantly degrades the quality of transmitted information. Existing technologies, such as artificial neural networks (ANNs) and spatial light modulators (SLMs), have achieved limited success in reconstructing distorted images after MMF transmission. Despite these advancements, the direct optical transmission of undistorted images through MMFs using micron-scale integrated optical components has remained an elusive goal in optical research.
Addressing the longstanding challenges of multi-mode fiber (MMF) transmission, the research team led by Prof. Qiming Zhang and Associate Prof. Haoyi Yu from the School of Artificial Intelligence Science and Technology (SAIST) at the University of Shanghai for Science and Technology (USST) has introduced a groundbreaking solution. The study is published in the journal Nature Photonics.
