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Abstract CRISPR gene editing offers unprecedented genomic and transcriptomic control for precise regulation of cell function and phenotype. However, delivering the necessary CRISPR components to therapeutically relevant cell types without cytotoxicity or unexpected side effects remains challenging. The RALA cell penetrating peptide is an amphiphilic peptide that self‐assembles into nanoparticles through electrostatic interactions with anionic molecules and delivers them across the cell membrane. Given the low cytotoxicity, versatility, and competitive transfection rates of RALA, we aimed to establish this peptide as a new CRISPR delivery system in a wide range of molecular formats across different editing modalities. We report that RALA effectively encapsulated and delivered CRISPR DNAs, RNAs, and ribonucleic proteins (RNPs) to primary mesenchymal stem cells (MSCs), outperforming commercially available reagents. We then used the RALA peptide for the knock‐in and knock‐out of reporter genes into primary MSCs and the transcriptional activation of therapeutically relevant genes. Finally, we demonstrate in vivo gene editing using RALA to knock‐out luciferase and GFP in a reporter mouse model. In summary, we establish RALA as a powerful tool for safer and more effective delivery of CRISPR machinery in multiple cargo formats for a wide range of ex vivo and in vivo gene editing strategies. 

ABSTRACT Real‐time hybrid simulation (RTHS) is an experimental testing methodology that divides a structural system into an analytical and an experimental substructure. The analytical substructure is modeled numerically, and the experimental substructure is modeled physically in the laboratory. The two substructures are kinematically linked together at their interface degrees of freedom, and the coupled equations of motion are solved in real‐time to obtain the response of the complete system. A key challenge in applying RTHS to large or complex structures is the limited availability of physical devices, which makes it difficult to represent all required experimental components simultaneously. The present study addresses this challenge by introducing Online Cyber‐Physical Neural Network (OCP‐NN) models–neural network‐based models of physical devices that are integrated in real‐time with the experimental substructure during an RTHS. The OCP‐NN framework leverages real‐time data from a single physical device (i.e., the experimental substructure) to replicate its behavior at other locations in the system, thereby significantly reducing the need for multiple physical devices. The proposed method is demonstrated through RTHS of a two‐story reinforced concrete frame subjected to seismic excitation and equipped with Banded Rotary Friction Dampers (BRFDs) in each story. BRFDs are challenging to model numerically due to their complex behavior which includes backlash, stick‐slip phenomena, and inherent device dynamics. Consequently, BRFDs were selected to demonstrate the proposed framework. In the RTHS, one BRFD is modeled physically by the experimental substructure, while the other is represented by the OCP‐NN model. The results indicate that the OCP‐NN model can accurately capture the behavior of the device in real‐time. This approach offers a practical solution for improving RTHS of complex structural systems with limited experimental resources. 

A low-cost testing procedure is proposed to measure the orientation and range of an acoustic array transmitter at the bottom of the lake or ocean in relation to a surface receiver. Since the GPS measurements are available only on the surface, an inertial measurement unit (IMU) and a depth sensor are used on the transmitter to track the orientation and depth when the transmitter platform is lowered to the deployment position underwater. Procedures for post-experiment data processing are detailed to combine the GPS observations and IMU measurements which yield the locations of the surface receiver relative to the bottom transmitter. Using GPS corrections and long-term stable correction of the IMU sensor, the proposed procedure achieves meter-level accuracy of the localization measurement. 

ABSTRACT Diacylglycerol O‐acyltransferase 1 (DGAT1) is an integral membrane protein that uses acyl‐coenzyme A (acyl‐CoA) and diacylglycerol (DAG) to catalyze the formation of triacylglycerides (TAGs). The acyl transfer reaction occurs between the activated carboxylate group of the fatty acid and the free hydroxyl group on the glycerol backbone of DAG. However, how the two substrates enter DGAT1's catalytic reaction chamber and interact with DGAT1 remains elusive. This study aims to explore the structural basis of DGAT1's substrate recognition by investigating each substrate's pathway to the reaction chamber. Using a human DGAT1 cryo‐EM structure in complex with an oleoyl‐CoA substrate, we designed two different all‐atom molecular dynamics (MD) simulation systems: DGAT1^away(both acyl‐CoA and DAG away from the reaction chamber) and DGAT1^bound(acyl‐CoA bound in and DAG away from the reaction chamber). Our DGAT1^awaysimulations reveal that acyl‐CoA approaches the reaction chamber via interactions with positively charged residues in transmembrane helix 7. DGAT1^boundsimulations show DAGs entering into the reaction chamber from the cytosol leaflet. The bound acyl‐CoA's fatty acid lines up with the headgroup of DAG, which appears to be competent to TAG formation. We then converted them into TAG and coenzyme (CoA) and used adaptive biasing force (ABF) simulations to explore the egress pathways of the products. We identify their escape routes, which are aligned with their respective entry pathways. Visualization of the substrate and product pathways and their interactions with DGAT1 is expected to guide future experimental design to better understand DGAT1 structure and function. 

ABSTRACT Peptides are widely used in biomaterials due to their ease of synthesis, ability to signal cells, and modify the properties of biomaterials. A key benefit of using peptides is that they are natural substrates for cell‐secreted enzymes, which creates the possibility of utilizing cell‐secreted enzymes for tuning cell–material interactions. However, these enzymes can also induce unwanted degradation of bioactive peptides in biomaterials, or in peptide therapies. Liquid chromatography–mass spectrometry (LC–MS) is a widely used, powerful methodology that can separate complex mixtures of molecules and quantify numerous analytes within a single run. There are several challenges in using LC–MS for the multiplexed quantification of cell‐induced peptide degradation, including the need for nondegradable internal standards and the identification of optimal sample storage conditions. Another problem is that cell culture media and biological samples typically contain both proteins and lipids that can accumulate on chromatography columns and degrade their performance. Removing these constituents can be expensive, time‐consuming, and increases sample variability. However, loading unpurified samples onto the column without removing lipids and proteins will foul the column. Here, we show that directly injecting complex, unpurified samples onto the LC–MS without any purification enables rapid and accurate quantification of peptide concentration and that hundreds of LC–MS runs can be done on a single column without significantly diminishing the ability to quantify the degradation of peptide libraries. To understand how repeated injections degrade column performance, a model library was injected into the LC–MS hundreds of times. It was then determined that column failure is evident when hydrophilic peptides are no longer retained on the column and that failure can be easily identified by using standard peptide mixtures for column benchmarking. In total, this work introduces a simple and effective method for simultaneously quantifying the degradation of dozens of peptides in cell culture. By providing a streamlined and cost‐effective method for the direct quantification of peptide degradation in complex biological samples, this work enables more efficient assessment of peptide stability and functionality, facilitating the development of advanced biomaterials and peptide‐based therapies. 

Abstract Taking place naturally in a gas subject to a given wall temperature distribution, the “ghost effect” exhibits a rare kinetic effect beyond the prediction of classical fluid theory and Fourier law in such a classical problem in physics. As the Knudsen number goes to zero, the finite variation of temperature in the bulk is determined by an infinitesimal, ghost‐like velocity field, created by a givenfinitevariation of the tangential wall temperature as predicted by Maxwell's slip boundary condition. Mathematically, such a finite variation leads to the presence of a severe singularity and a Knudsen layer approximation in the fundamental energy estimate. Neither difficulty is within the reach of any existing PDE theory on the steady Boltzmann equation in a general 3D bounded domain. Consequently, in spite of the discovery of such a ghost effect from temperature variation in as early as 1960s, its mathematical validity has been a challenging and intriguing open question, causing confusion and suspicion. We settle this open question in affirmative if the temperature variation is small but finite, by developing a new framework with four major innovations as follows: (1) a key ‐Hodge decomposition and its corresponding local ‐conservation law eliminate the severe bulk singularity, leading to a reduced energy estimate; (2) a surprising gain in via momentum conservation and a dual Stokes solution; (3) the ‐conservation, energy conservation, and a coupled dual Stokes–Poisson solution reduces to an boundary singularity; (4) a crucial construction of ‐cutoff boundary layer eliminates such boundary singularity via new Hardy's and BV estimates. 

This paper presents the hardware implementation of a massive Multiple-Input Multiple-Output (MIMO) transmitter for underwater acoustic (UWA) communication capable of incorporating precoding or beamforming. The transmitter consists of baseband and passband processing modules implemented on an AMD-Xilinx All Programmable System-on-Chip (AP-SoC) architecture, frontend power amplifiers, and high-frequency transducers. While the number of channels can be easily scaled, the current hardware demonstrates a 16-channel transmitter at a carrier frequency of 115 kHz. Experiments in the lab and field show that passband beamforming and precoding are successfully transmitted through the 16 transducers. The receiver signal strengths, however, deviate largely from the free-space simulation of the beam patterns due to rich multipath reflections and imperfection in element spacing and omni-directionality of the transducers. 

ABSTRACT Hybridization occurs when different species mate and produce offspring. Although hybridization can have negative consequences for cognitive performance, the mechanisms underlying those effects are still poorly understood. A fundamental physiological process found in all animals studied to date that could be disrupted in hybrids is sleep. Given that mechanisms that occur within the brain during sleep may help maintain optimal cognitive performance, here we outline the potential impacts of hybridization on sleep and cognition. We suggest that sleep loss caused by hybridization could lead to negative impacts for neural and molecular mechanisms (e.g. neurogenesis, synaptic plasticity, and brain gene expression) associated with cognition, which may help explain some of the cognitive deficiency recently observed in hybrid birds. However, we acknowledge that these mechanisms may instead be directly impacted by hybridization, which in turn could also disrupt sleep with similar negative consequences for cognition. Limitations in sleep processes apparent in hybrids might influence hybrid fitness and therefore act as a post‐zygotic isolating barrier.