Search for: All records

Computational fluid dynamics (CFD) simulations are broadly used in many engineering and physics fields. CFD requires the solution of the Navier–Stokes (N-S) equations under complex flow and boundary conditions. However, applications of CFD simulations are computationally limited by the availability, speed, and parallelism of high-performance computing. To address this, machine learning techniques have been employed to create data-driven approximations for CFD to accelerate computational efficiency. Unfortunately, these methods predominantly depend on large labeled CFD datasets, which are costly to procure at the scale required for robust model development. In response, we introduce a weakly supervised approach that, through a multichannel input capturing boundary and geometric conditions, solves steady-state N-S equations. Our method achieves state-of-the-art results without relying on labeled simulation data, instead using a custom data-driven and physics-informed loss function and small-scale solutions to prime the model for solving the N-S equations. By training stacked models, we enhance resolution and predictability, yielding high-quality numerical solutions to N-S equations without hefty computational demands. Remarkably, our model, being highly adaptable, produces solutions on a 512 × 512 domain in a swift 7 ms, outpacing traditional CFD solvers by a factor of 1,000. This paves the way for real-time predictions on consumer hardware and Internet of Things devices, thereby boosting the scope, speed, and cost-efficiency of solving boundary-value fluid problems. 

In recent years, extracellular vesicles have become promising carriers as next-generation drug delivery platforms. Effective loading of exogenous cargos without compromising the extracellular vesicle membrane is a major challenge. Rapid squeezing through nanofluidic channels is a widely used approach to load exogenous cargoes into the EV through the nanopores generated temporarily on the membrane. However, the exact mechanism and dynamics of nanopore opening, as well as cargo loading through nanopores during the squeezing process remains unknown and it is impossible to visualize or quantify it experimentally due to the small size of the EV and the fast transient process. This paper developed a systemic algorithm to simulate nanopore formation and predict drug loading during extracellular vesicle (EV) squeezing by leveraging the power of coarse-grain (CG) molecular dynamics simulations with fluid dynamics. The EV CG beads are coupled with implicit the fluctuating lattice Boltzmann solvent. The effects of EV properties and various squeezing test parameters, such as EV size, flow velocity, channel width, and length, on pore formation and drug loading efficiency are analyzed. Based on the simulation results, a phase diagram is provided as a design guide for nanochannel geometry and squeezing velocity to generate pores on the membrane without damaging the EV. This method can be utilized to optimize the nanofluidic device configuration and flow setup to obtain desired drug loading into EVs. 

Abstract Microfluidic devices have found extensive applications in mechanical, biomedical, chemical, and materials research. However, the high initial cost, low resolution, inferior feature fidelity, poor repeatability, rough surface finish, and long turn-around time of traditional prototyping methods limit their wider adoption. In this study, a strategic approach to a deterministic fabrication process based on in-situ image analysis and intermittent flow control called image-guided in-situ maskless lithography (IGIs-ML), has been proposed to overcome these challenges. By using dynamic image analysis and integrated flow control, IGIs-ML provides superior repeatability and fidelity of densely packed features across a large area and multiple devices. This general and robust approach enables the fabrication of a wide variety of microfluidic devices and resolves critical proximity effect and size limitations in rapid prototyping. The affordability and reliability of IGIs-ML make it a powerful tool for exploring the design space beyond the capabilities of traditional rapid prototyping. 

Pain relief on-demand Chronic pain is a debilitating condition for which there are no effective treatments. The primary somatosensory cortex (S1) and the anterior cingulate cortex (ACC) are involved in decoding pain components, and electrical stimulation of the prefrontal cortex (PFC) has been shown to exert analgesic effects. Here, Sun et al. developed a multiregion brain-machine interface (BMI) able to detect pain from electrical signals in S1 and ACC and provide on-demand PFC stimulation. The BMI was able to accurately detect and treat acute and chronic pain in rats; the analgesic effects were stable over time. The results suggest that BMI approaches might be effective for treating chronic pain of different etiologies. 

DNA methylation is an important epigenetic modification required for the specific regulation of gene expression and the maintenance of genome stability in plants and animals. However, the mechanism of DNA demethylation remains largely unknown. Here, we show that two SGS3-like proteins, FACTOR OF DNA DEMETHYLATION 1 (FDDM1) and FDDM2, negatively affect the DNA methylation levels at ROS1-dependend DNA loci in Arabidopsis. FDDM1 binds dsRNAs with 5′ overhangs through its XS (rice gene X and SGS3) domain and forms a heterodimer with FDDM2 through its XH (rice gene X Homology) domain. A lack of FDDM1 or FDDM2 increased DNA methylation levels at several ROS1-dependent DNA loci. However, FDDM1 and FDDM2 may not have an additive effect on DNA methylation levels. Moreover, the XS and XH domains are required for the function of FDDM1. Taken together, these results suggest that FDDM1 and FDDM2 act as a heterodimer to positively modulate DNA demethylation. Our finding extends the function of plant-specific SGS3-like proteins. 

Abstract Low-pressure nonthermal flowing plasmas are widely used for the gas-phase synthesis of nanoparticles and quantum dots of materials that are difficult or impractical to synthesize using other techniques. To date, the impact of temporary electrostatic particle trapping in these plasmas has not been recognized, a process that may be leveraged to control particle properties. Here, we present experimental and computational evidence that, during their growth in the plasma, sub-10 nm silicon particles become temporarily confined in an electrostatic trap in radio-frequency excited plasmas until they grow to a size at which the increasing drag force imparted by the flowing gas entrains the particles, carrying them out of the trap. We demonstrate that this trapping enables the size filtering of the synthesized particles, leading to highly monodisperse particle sizes, as well as the electrostatic focusing of the particles onto the reactor centerline. Understanding of the mechanisms and utilization of such particle trapping will enable the design of plasma processes with improved size control and the ability to grow heterostructured nanoparticles. 

During the COVID-19 pandemic, mathematical modeling of disease transmission has become a cornerstone of key state decisions. To advance the state-of-the-art host viral modeling to handle future pandemics, many scientists working on related issues assembled to discuss the topics. These discussions exposed the reproducibility crisis that leads to inability to reuse and integrate models. This document summarizes these discussions, presents difficulties, and mentions existing efforts towards future solutions that will allow future model utility and integration. We argue that without addressing these challenges, scientists will have diminished ability to build, disseminate, and implement high-impact multi-scale modeling that is needed to understand the health crises we face. 

Chronic pain is characterized by discrete pain episodes of unpredictable frequency and duration. This hinders the study of pain mechanisms and contributes to the use of pharmacological treatments associated with side effects, addiction and drug tolerance. Here, we show that a closed-loop brain–machine interface (BMI) can modulate sensory-affective experiences in real time in freely behaving rats by coupling neural codes for nociception directly with therapeutic cortical stimulation. The BMI decodes the onset of nociception via a state-space model on the basis of the analysis of online-sorted spikes recorded from the anterior cingulate cortex (which is critical for pain processing) and couples real-time pain detection with optogenetic activation of the prelimbic prefrontal cortex (which exerts top–down nociceptive regulation). In rats, the BMI effectively inhibited sensory and affective behaviours caused by acute mechanical or thermal pain, and by chronic inflammatory or neuropathic pain. The approach provides a blueprint for demand-based neuromodulation to treat sensory-affective disorders, and could be further leveraged for nociceptive control and to study pain mechanisms.