Sonication-enabled rapid production of stable liquid metal nanoparticles grafted with poly(1- octadecene-alt-maleic anhydride) in aqueous solutions

Gallium-based liquid metals are attractive due to their unique combination of metallic and fluidic properties. Liquid metal nanoparticles (LM NPs), produced readily using sonication, find use in soft electronics, drug delivery, and other applications. However, LM NPs in aqueous solutions tend to oxidize and precipitate over time, which hinders their utility in systems that require long-term stability. Here, we introduce a facile route to rapidly produce an aqueous suspension of stable LM NPs within five minutes. We accomplish this by dissolving poly(1-octadecene-alt-maleic anhydride) (POMA) in toluene and mixing with deionized water in the presence of a liquid metal (LM). Sonicating the mixture results in the formation of toluene-POMA emulsions that embed the LM NPs; as the toluene evaporates, POMA coats the particles. Due to the POMA hydrophobic coating, the LM NPs remain stable in biological buffers for at least 60 days without noticeable oxidation, as confirmed by dynamic light scattering and transmission electron microscopy. Further stabilization is achieved by tuning the LM composition. This paper elucidates the stabilization mechanisms. The stable LM NPs possess the potential to advance the use of LM in biomedical applications

Mechanism of fatigue performance enhancement in a laser sintered superhard nanoparticles reinforced nanocomposite followed by laser shock peening

This study investigates the fundamental mechanism of fatigue performance enhancement during a novel hybrid manufacturing process, which combines laser sintering of superhard nanoparticles integrated nanocomposites and laser shock peening (LSP). Through laser sintering, TiN nanoparticles are integrated uniformly into iron matrix to form a nanocomposite layer near the surface of AISI4140 steel. LSP is then performed on the nanocomposite layer to generate interaction between nanoparticles and shock waves. The fundamental mechanism of fatigue performance enhancement is discussed in this paper. During laser shock interaction with the nanocomposites, the existence of nanoparticles increases the dislocation density and also helps to pin the dislocation movement. As a result, both dislocation density and residual stress are stabilized, which is beneficial for fatigue performance. (C) 2013 American Institute of Physics. [http://dx.doi.org/10.1063/1.4799154

Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform

We present Rhino, a system for accelerating tensor programs with automatic parallelization on AI platform for real production environment. It transforms a tensor program written for a single device into an equivalent distributed program that is capable of scaling up to thousands of devices with no user configuration. Rhino firstly works on a semantically independent intermediate representation of tensor programs, which facilitates its generalization to unprecedented applications. Additionally, it implements a task-oriented controller and a distributed runtime for optimal performance. Rhino explores on a complete and systematic parallelization strategy space that comprises all the paradigms commonly employed in deep learning (DL), in addition to strided partitioning and pipeline parallelism on non-linear models. Aiming to efficiently search for a near-optimal parallel execution plan, our analysis of production clusters reveals general heuristics to speed up the strategy search. On top of it, two optimization levels are designed to offer users flexible trade-offs between the search time and strategy quality. Our experiments demonstrate that Rhino can not only re-discover the expert-crafted strategies of classic, research and production DL models, but also identify novel parallelization strategies which surpass existing systems for novel models

G-quadruplex structures trigger RNA phase separation

Liquid–liquid phase separation plays an important role in a variety of cellular processes, including the formation of membrane-less organelles, the cytoskeleton, signalling complexes, and many other biological supramolecular assemblies. Studies on the molecular basis of phase separation in cells have focused on protein-driven phase separation. In contrast, there is limited understanding on how RNA specifically contributes to phase separation. Here, we described a phase-separation-like phenomenon that SHORT ROOT (SHR) RNA undergoes in cells. We found that an RNA G-quadruplex (GQ) forms in SHR mRNA and is capable of triggering RNA phase separation under physiological conditions, suggesting that GQs might be responsible for the formation of the SHR phase-separation-like phenomenon in vivo. We also found the extent of GQ-triggered-phase-separation increases on exposure to conditions which promote GQ. Furthermore, GQs with more G-quartets and longer loops are more likely to form phase separation. Our studies provide the first evidence that RNA can adopt structural motifs to trigger and/or maintain the specificity of RNA-driven phase separation

Assessment of particle size magnifier inversion methods to obtain the particle size distribution from atmospheric measurements

Accurate measurements of the size distribution of atmospheric aerosol nanoparticles are essential to build an understanding of new particle formation and growth. This is particularly crucial at the sub-3 nm range due to the growth of newly formed nanoparticles. The challenge in recovering the size distribution is due its complexity and the fact that not many instruments currently measure at this size range. In this study, we used the particle size magnifier (PSM) to measure atmospheric aerosols. Each day was classified into one of the following three event types: a new particle formation (NPF) event, a non-event or a haze event. We then compared four inversion methods (stepwise, kernel, Hagen-Alofs and expectation-maximization) to determine their feasibility to recover the particle size distribution. In addition, we proposed a method to pretreat the measured data, and we introduced a simple test to estimate the efficacy of the inversion itself. Results showed that all four methods inverted NPF events well; however, the stepwise and kernel methods fared poorly when inverting non-events or haze events. This was due to their algorithm and the fact that, when encountering noisy data (e.g. air mass fluctuations or low sub-3 nm particle concentrations) and under the influence of larger particles, these methods overestimated the size distribution and reported artificial particles during inversion. Therefore, using a statistical hypothesis test to discard noisy scans prior to inversion is an important first step toward achieving a good size distribution. After inversion, it is ideal to compare the integrated concentration to the raw estimate (i.e. the concentration difference at the lowest supersaturation and the highest supersaturation) to ascertain whether the inversion itself is sound. Finally, based on the analysis of the inversion methods, we provide procedures and codes related to the PSM data inversion.Peer reviewe

Functional Liquid Metal Nanoparticles Produced by Liquid-Based Nebulization

Functional liquid metal nanoparticles (NPs), produced from eutectic alloys of gallium, promise new horizons in the fields of sensors, microfluidics, flexible electronics, catalysis, and biomedicine. Here, the development of a vapor cavity generating ultrasonic platform for nebulizing liquid metal within aqueous media for the one-step production of stable and functional liquid metal NPs is shown. The size distribution of the NPs is fully characterized and it is demonstrated that various macro and small molecules can also be grafted onto these liquid metal NPs during the liquid-based nebulization process. The cytotoxicity of the NPs grafted with different molecules is further explored. Moreover, it is shown that it is possible to control the thickness of the oxide layer on the produced NPs using electrochemistry that can be embedded within the platform. It is envisaged that this platform can be adapted as a cost-effective and versatile device for the rapid production of functional liquid metal NPs for future liquid metal-based optical, electronic, catalytic, and biomedical applications

Nanowired human cardiac organoid transplantation enables highly efficient and effective recovery of infarcted hearts

Human cardiac organoids hold remarkable potential for cardiovascular disease modeling and human pluripotent stem cell–derived cardiomyocyte (hPSC-CM) transplantation. Here, we show cardiac organoids engineered with electrically conductive silicon nanowires (e-SiNWs) significantly enhance the therapeutic efficacy of hPSC-CMs to treat infarcted hearts. We first demonstrated the biocompatibility of e-SiNWs and their capacity to improve cardiac microtissue engraftment in healthy rat myocardium. Nanowired human cardiac organoids were then engineered with hPSC-CMs, nonmyocyte supporting cells, and e-SiNWs. Nonmyocyte supporting cells promoted greater ischemia tolerance of cardiac organoids, and e-SiNWs significantly improved electrical pacing capacity. After transplantation into ischemia/reperfusion–injured rat hearts, nanowired cardiac organoids significantly improved contractile development of engrafted hPSC-CMs, induced potent cardiac functional recovery, and reduced maladaptive left ventricular remodeling. Compared to contemporary studies with an identical injury model, greater functional recovery was achieved with a 20-fold lower dose of hPSC-CMs, revealing therapeutic synergy between conductive nanomaterials and human cardiac organoids for efficient heart repair

Periplasmic biomineralization for semi-artificial photosynthesis

Semiconductor-based biointerfaces are typically established either on the surface of the plasma membrane or within the cytoplasm. In Gram-negative bacteria, the periplasmic space, characterized by its confinement and the presence of numerous enzymes and peptidoglycans, offers additional opportunities for biomineralization, allowing for nongenetic modulation interfaces. We demonstrate semiconductor nanocluster precipitation containing single- and multiple-metal elements within the periplasm, as observed through various electron- and x-ray-based imaging techniques. The periplasmic semiconductors are metastable and display defect-dominant fluorescent properties. Unexpectedly, the defect-rich (i.e., the low-grade) semiconductor nanoclusters produced in situ can still increase adenosine triphosphate levels and malate production when coupled with photosensitization. We expand the sustainability levels of the biohybrid system to include reducing heavy metals at the primary level, building living bioreactors at the secondary level, and creating semi-artificial photosynthesis at the tertiary level. The biomineralization-enabled periplasmic biohybrids have the potential to serve as defect-tolerant platforms for diverse sustainable applications

Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge

Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" \unicode{x2013} there are a few common findings followed by many more relatively rare conditions. In chest radiography, diagnosis is both a long-tailed and multi-label problem, as patients often present with multiple findings simultaneously. While researchers have begun to study the problem of long-tailed learning in medical image recognition, few have studied the interaction of label imbalance and label co-occurrence posed by long-tailed, multi-label disease classification. To engage with the research community on this emerging topic, we conducted an open challenge, CXR-LT, on long-tailed, multi-label thorax disease classification from chest X-rays (CXRs). We publicly release a large-scale benchmark dataset of over 350,000 CXRs, each labeled with at least one of 26 clinical findings following a long-tailed distribution. We synthesize common themes of top-performing solutions, providing practical recommendations for long-tailed, multi-label medical image classification. Finally, we use these insights to propose a path forward involving vision-language foundation models for few- and zero-shot disease classification