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Researchers have successfully realized logic gates using DNA crystal engineering, a monumental step forward in DNA computation. Their findings were published in Advanced Materials. Using DNA double crossover-like motifs as building blocks, they constructed complex 3D crystal architectures. The logic gates were implemented in large ensembles of these 3D DNA crystals, and the outputs were visible through the formation of macroscopic crystals. This advancement could pave the way for DNA-based biosensors, offering easy readouts for various applications. The study demonstrates the power of DNA computing, capable of executing massively parallel information processing at a molecular level, while maintaining compatibility with biological systems.

Journal Article

Implementing Logic Gates by DNA Crystal Engineering

Abstract:

DNA self-assembly computation is attractive for its potential to perform massively parallel information processing at the molecular level while at the same time maintaining its natural biocompatibility. It has been extensively studied at the individual molecule level, but not as much as ensembles in 3D. Here, the feasibility of implementing logic gates, the basic computation operations, in large ensembles: macroscopic, engineered 3D DNA crystals is demonstrated. The building blocks are the recently developed DNA double crossover-like (DXL) motifs. They can associate with each other via sticky-end cohesion. Common logic gates are realized by encoding the inputs within the sticky ends of the motifs. The outputs are demonstrated through the formation of macroscopic crystals that can be easily observed. This study points to a new direction of construction of complex 3D crystal architectures and DNA-based biosensors with easy readouts.

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 unstable 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.

Journal Article

Suppression of superoxide/hydrogen peroxide production at mitochondrial site IQ decreases fat accumulation, improves glucose tolerance and normalizes fasting insulin concentration in mice fed a high-fat diet

Abstract

A spinal cord injury interrupts the communication between the brain and the region of the spinal cord that produces walking, leading to paralysis. Here, we restored this communication with a digital bridge between the brain and spinal cord that enabled an individual with chronic tetraplegia to stand and walk naturally in community settings. This brain–spine interface (BSI) consists of fully implanted recording and stimulation systems that establish a direct link between cortical signals and the analogue modulation of epidural electrical stimulation targeting the spinal cord regions involved in the production of walking. A highly reliable BSI is calibrated within a few minutes. This reliability has remained stable over one year, including during independent use at home. The participant reports that the BSI enables natural control over the movements of his legs to stand, walk, climb stairs and even traverse complex terrains. Moreover, neurorehabilitation supported by the BSI improved neurological recovery. The participant regained the ability to walk with crutches overground even when the BSI was switched off. This digital bridge establishes a framework to restore natural control of movement after paralysis.

“We want to know how memories are made and how they fail to be made in people with memory disorders like Alzheimer’s disease,” said Mark Reimers, an associate professor in the College of Natural Science and Institute for Quantitative Health Sciences and Engineering. “We’d like to investigate and track the evolution of a memory over time and even observe how things get mixed up in everyday memory.”

Currently, high-resolution brain imaging techniques can capture only a few hundred individual neurons — the nerve cells that transmit electrical signals throughout the body — at a time. Starting with some initial seed money from the director of IQHSE, Christopher Contag, and MSU’s neuroscience program, Reimers and his co-investigator Christian Burgess at the University of Michigan were able to develop a prototype of the imaging system that has the potential to image 10,000 to 20,000 neurons, giving researchers an unprecedented view of brain activity in real time while it is making and recalling memories. This research has led to a three-year $750,000 grant from the Air Force Office of Scientific Research.

Abstract

Diffraction-limited optical imaging through scattering media has the potential to transform many applications such as airborne and space-based imaging (through the atmosphere), bioimaging (through skin and human tissue), and fiber-based imaging (through fiber bundles). Existing wavefront shaping methods can image through scattering media and other obscurants by optically correcting wavefront aberrations using high-resolution spatial light modulators—but these methods generally require (i) guidestars, (ii) controlled illumination, (iii) point scanning, and/or (iv) statics scenes and aberrations. We propose neural wavefront shaping (NeuWS), a scanning-free wavefront shaping technique that integrates maximum likelihood estimation, measurement modulation, and neural signal representations to reconstruct diffraction-limited images through strong static and dynamic scattering media without guidestars, sparse targets, controlled illumination, nor specialized image sensors. We experimentally demonstrate guidestar-free, wide field-of-view, high-resolution, diffraction-limited imaging of extended, nonsparse, and static/dynamic scenes captured through static/dynamic aberrations.

Journal Article

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Papers:

Neuronal diagram of an adult (fruit fly) brain

A consensus cell type atlas from multiple connectomes reveals principles of circuit stereotypy and variation

Explore the connectome: https://codex.flywire.ai

Abstract

Genomic (DNA) sequences encode an enormous amount of information for gene regulation and protein synthesis. Similar to natural language models, researchers have proposed foundation models in genomics to learn generalizable features from unlabeled genome data that can then be fine-tuned for downstream tasks such as identifying regulatory elements. Due to the quadratic scaling of attention, previous Transformer-based genomic models have used 512 to 4k tokens as context (<0.001% of the human genome), significantly limiting the modeling of long-range interactions in DNA. In addition, these methods rely on tokenizers to aggregate meaningful DNA units, losing single nucleotide resolution where subtle genetic variations can completely alter protein function via single nucleotide polymorphisms (SNPs). Recently, Hyena, a large language model based on implicit convolutions was shown to match attention in quality while allowing longer context lengths and lower time complexity. Leveraging Hyenas new long-range capabilities, we present HyenaDNA, a genomic foundation model pretrained on the human reference genome with context lengths of up to 1 million tokens at the single nucleotide-level, an up to 500x increase over previous dense attention-based models. HyenaDNA scales sub-quadratically in sequence length (training up to 160x faster than Transformer), uses single nucleotide tokens, and has full global context at each layer. We explore what longer context enables - including the first use of in-context learning in genomics for simple adaptation to novel tasks without updating pretrained model weights. On fine-tuned benchmarks from the Nucleotide Transformer, HyenaDNA reaches state-of-the-art (SotA) on 12 of 17 datasets using a model with orders of magnitude less parameters and pretraining data. On the GenomicBenchmarks, HyenaDNA surpasses SotA on all 8 datasets on average by +9 accuracy points.

Huggingface link

ArXiv Paper

New foundation agent learns to operate different robotic arms, solves tasks from as few as 100 demonstrations, and improves from self-generated data.

Robots are quickly becoming part of our everyday lives, but they’re often only programmed to perform specific tasks well. While harnessing recent advances in AI could lead to robots that could help in many more ways, progress in building general-purpose robots is slower in part because of the time needed to collect real-world training data.

Our latest paper introduces a self-improving AI agent for robotics, RoboCat, that learns to perform a variety of tasks across different arms, and then self-generates new training data to improve its technique.

Previous research has explored how to develop robots that can learn to multi-task at scale and combine the understanding of language models with the real-world capabilities of a helper robot. RoboCat is the first agent to solve and adapt to multiple tasks and do so across different, real robots.

RoboCat learns much faster than other state-of-the-art models. It can pick up a new task with as few as 100 demonstrations because it draws from a large and diverse dataset. This capability will help accelerate robotics research, as it reduces the need for human-supervised training, and is an important step towards creating a general-purpose robot

The rat kidney was peculiarly beautiful — an edgeless viscera about the size of a quarter, gemstone-like and gleaming as if encased in pure glass.

It owed its veneer to a frosty descent in liquid nitrogen vapor to minus 150-degrees Celsius, a process known as vitrification, that shocked the kidney into an icy state of suspended animation. Then researchers at the University of Minnesota restarted the kidney’s biological clock, rewarming it before transplanting it back into a live rat — who survived the ordeal.

In all, five rats received a vitrified-then-thawed kidney in a study whose results were published this month in Nature Communications. It’s the first time scientists have shown it’s possible to successfully and repeatedly transplant a life-sustaining mammalian organ after it has been rewarmed from this icy metabolic arrest. Outside experts unequivocally called the results a seminal milestone for the field of organ preservation.

Journal Article:

Vitrification and nanowarming enable long-term organ cryopreservation and life-sustaining kidney transplantation in a rat model

To accelerate development of useful new materials, researchers are building a new kind of automated lab that uses robots guided by artificial intelligence.

“Our vision is using AI to discover the materials of the future,” said Yan Zeng, a staff scientist leading the A-Lab at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab). The “A” in A-Lab is deliberately ambiguous, standing for artificial intelligence (AI), automated, accelerated, and abstracted, among others.

Scientists have computationally predicted hundreds of thousands of novel materials that could be promising for new technologies – but testing to see whether any of those materials can be made in reality is a slow process. Enter A-Lab, which can process 50 to 100 times as many samples as a human every day and use AI to quickly pursue promising finds.

A-Lab could help identify and fast-track materials for several research areas, such as solar cells, fuel cells, thermoelectrics (materials that generate energy from temperature differences), and other clean energy technologies. To start, researchers will focus on finding new materials for batteries and energy storage, addressing critical needs for an affordable, equitable, and sustainable energy supply.

For most of the history of life on Earth, genetic information has been carried in a code that specifies just 20 amino acids. Amino acids are the building blocks of proteins, which do most of the heavy lifting in the cell; their side-chains govern protein folding, interactions and chemical activities. By limiting the available side chains, nature effectively restricts the kinds of reaction that proteins can perform.

As a doctoral student in the 1980s, Peter Schultz found himself wondering why nature had restricted itself in this way — and set about trying to circumvent this limitation. Several years later, as a professor at the University of California, Berkeley, Schultz and his team managed to do so by tinkering with the machinery of protein synthesis. Although confined to a test tube, the work marked a key early success in efforts to hack the genetic code.

Since then, many researchers have followed in Schultz’s footsteps, tweaking the cellular apparatus for building proteins both to alter existing macromolecules and to create polymers from entirely new building blocks. The resulting molecules can be used in research and for the development of therapeutics and materials. But it’s been a hard slog, because protein synthesis is a crucial cellular function that cannot easily be changed.

Significance

We demonstrate the highest-resolution MR images ever obtained of the mouse brain. The diffusion tensor images (DTI) @ 15 μm spatial resolution are 1,000 times the resolution of most preclinical rodent DTI/MRI. Superresolution track density images are 27,000 times that of typical preclinical DTI/MRI. High angular resolution yielded the most detailed MR connectivity maps ever generated. High-performance computing pipelines merged the DTI with light sheet microscopy of the same specimen, providing a comprehensive picture of cells and circuits. The methods have been used to demonstrate how strain differences result in differential changes in connectivity with age. We believe the methods will have broad applicability in the study of neurodegenerative diseases.

Abstract

We have developed workflows to align 3D magnetic resonance histology (MRH) of the mouse brain with light sheet microscopy (LSM) and 3D delineations of the same specimen. We start with MRH of the brain in the skull with gradient echo and diffusion tensor imaging (DTI) at 15 μm isotropic resolution which is ~ 1,000 times higher than that of most preclinical MRI. Connectomes are generated with superresolution tract density images of ~5 μm. Brains are cleared, stained for selected proteins, and imaged by LSM at 1.8 μm/pixel. LSM data are registered into the reference MRH space with labels derived from the ABA common coordinate framework. The result is a high-dimensional integrated volume with registration (HiDiver) with alignment precision better than 50 µm. Throughput is sufficiently high that HiDiver is being used in quantitative studies of the impact of gene variants and aging on mouse brain cytoarchitecture and connectomics.