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Small—but finite—fluid inertia can be leveraged to generate steady flows out of liquid vibrations around an immersed interface. In engineering, external high-frequency drivers $\left({10}^2\text{–}{10}^5\mathrm{Hz}\right)$allow this inertial rectification phenomenon, known as viscous streaming, to be employed in micron-scale devices for precise flow control, particle manipulation, and spatially controlled chemistry. However, beyond artificial settings, streaming has been hypothesized to be accessible by larger-scale biological systems pertaining to lower frequencies. Then millimeter-size organisms that oscillate or pulsate cilia and appendages in the 1 to $10\mathrm{Hz}$range may be able to rectify surrounding flows, for feeding or locomotion, removing the need for external actuators, tethers, or tubing. Motivated by this potential for bio-hybrid robotic applications and biophysical exploration, here we demonstrate an living system able to produce streaming flows endogenously, autonomously, and unassisted. Computationally informed, our biological device generates oscillatory flows through the cyclic contractions of an engineered muscle tissue, shaped in the form of a torus and suspended in fluid within a microparticle image velocimetry setup. Flow patterns consistent with streaming simulations are observed for low-frequency muscle contractions $(2–4\mathrm{Hz})$, either spontaneous or light-induced, illustrating system autonomy and controllability, respectively. Thus, by connecting tissue engineering with hydrodynamics, this work provides experimental evidence of biologically powered streaming in untethered, millimeter-scale living systems, endowing bio-hybrid technology with inertial microfluidic capabilities. It also illustrates the potential of combining bio-hybrid platforms and simulations to advance both biophysical understanding and fluid mechanics. Published by the American Physical Society2025 

Image super-resolution (SR) is widely used on mobile devices to enhance user experience. However, neural networks used for SR are computationally expensive, posing challenges for mobile devices with limited computing power. A viable solution is to use heterogeneous processors on mobile devices, especially the specialized hardware AI accelerators, for SR computations, but the reduced arithmetic precision on AI accelerators can lead to degraded perceptual quality in upscaled images. To address this limitation, in this paper we present SR For Your Eyes (FYE-SR), a novel image SR technique that enhances the perceptual quality of upscaled images when using heterogeneous processors for SR computations. FYESR strategically splits the SR model and dispatches different layers to heterogeneous processors, to meet the time constraint of SR computations while minimizing the impact of AI accelerators on image quality. Experiment results show that FYE-SR outperforms the best baselines, improving perceptual image quality by up to 2x, or reducing SR computing latency by up to 5.6x with on-par image quality. 

Eukaryotic elongation factors (eEFs) are protein factors that mediate the extension of peptide chain, among which eukaryotic elongation factor 1 alpha (eEF1A) is one of the most abundant protein synthesis factors. Previously we showed that the P3 protein of Soybean mosaic virus (SMV), one of the most destructive and successful viral pathogens of soybean, targets a component of the soybean translation elongation complex to facilitate its pathogenesis. Here, we conducted a systematic analyses of the soybeaneEF(GmeEF) gene family in soybean and examinedits role in virus resistance. In this study, GmeEF family members were identified and characterized based on sequence analysis. The 42 members, which were unevenly distributed across the 15 chromosomes, were renamed according to their chromosomal locations. The GmeEF members were further divided into 12 subgroups based on conserved motif, gene structure, and phylogenetic analyses. Analysis of the promoter regions showed conspicuous presence of myelocytomatosis (MYC) and ethylene-responsive (ERE) cis-acting elements, which are typically involved in drought and phytohormone response, respectively, and thereby in plant stress response signaling. Transcriptome data showed that the expression of 15GmeEFgene family members changed significantly in response to SMV infection. To further examine EF1A function in pathogen response, three different Arabidopsis mutants carrying T-DNA insertions in orthologous genes were analyzed for their response to Turnip crinkle virus (TCV) and Cucumber mosaic virus (CMV). Results showed that there was no difference in viral response between the mutants and the wild type plants. This study provides a systematic analysis of theGmeEFgene family through analysis of expression patterns and predicted protein features. Our results lay a foundation for understanding the role ofeEFgene in soybean anti-viral response. 

Fine-tuning is essential to adapting pre-trained large language models to downstream applications. With the increasing popularity of LLM-enabled applications, fine-tuning has been performed intensively worldwide, incurring a tremendous amount of computing costs that correspond to big carbon footprint and environmental impact. Mitigating such environmental impact directly correlates to reducing the fine-tuning FLOPs. Existing fine-tuning schemes focus on either saving memory or reducing the overhead of computing weight updates, but cannot achieve sufficient FLOPs reduction due to their ignorance of the training cost in backpropagation. To address this limitation, in this paper we present GreenTrainer, a new technique that minimizes the FLOPs of LLM fine-tuning via adaptive backpropagation, which adaptively selects the most appropriate set of LLM tensors for fine-tuning based on their importance and backpropagation cost in training. Experiment results show that GreenTrainer can save up to 64% training FLOPs compared to full fine-tuning, without any noticeable accuracy loss. Compared to the existing schemes such as Prefix Tuning and LoRA, GreenTrainer can achieve up to 4% improvement of model accuracy, with on-par FLOPs reduction. 

Abstract High-entropy alloys (HEAs) provide new research avenues for alloy combinations in the periodic table, opening numerous possibilities in novel-alloy applications. However, their electrical characteristics have been relatively underexplored. The challenge in establishing an HEA electrical conductivity model lies in the changes in electronic characteristics caused by lattice distortion and complexity of nanostructures. Here we show a low-frequency electrical conductivity model for the Nb-Mo-Ta-W HEA system. The cocktail effect is found to explain trends in electrical-conductivity changes in HEAs, while the magnitude of the reduction is understood by the calculated plasma frequency, free electron density, and measured relaxation time by terahertz spectroscopy. As a result, the refractory HEA Nb₁₅Mo₃₅Ta₁₅W₃₅thin film exhibits both high hardness and excellent conductivity. This combination of Nb₁₅Mo₃₅Ta₁₅W₃₅makes it suitable for applications in atomic force microscopy probe coating, significantly improving their wear resistance and atomic-scale image resolution.