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Soft phototactic swimmer based on self-sustained hydrogel oscillator.
Oscillations are widely found in living organisms to generate propulsion-based locomotion often driven by constant ambient conditions, such as phototactic movements. Such environment-powered and environment-directed locomotions may advance fully autonomous remotely steered robots. However, most man-made oscillations require nonconstant energy input and cannot perform environment-dictated movement. Here, we report a self-sustained soft oscillator that exhibits perpetual and untethered locomotion as a phototactic soft swimming robot, remotely fueled and steered by constant visible light. This particular out-of-equilibrium actuation arises from a self-shadowing-enabled negative feedback loop inherent in the dynamic light-material interactions, promoted by the fast and substantial volume change of the photoresponsive hydrogel. Our analytical model and governing equation unveil the oscillation mechanism and design principle with key parameters identified to tune the dynamics. On this autonomous oscillator platform, we establish a broadly applicable principle for converting a continuous input into a discontinuous output. The modular design can be customized to accommodate various forms of input energy and to generate diverse oscillatory behaviors. The hydrogel oscillator showcases agile life-like omnidirectional motion in the entire three-dimensional space with near-infinite degrees of freedom. The large force generated by the powerful and long-lasting oscillation can sufficiently overcome water damping and effectively self-propel away from a light source. Such a hydrogel oscillator-based all-soft swimming robot, named OsciBot, demonstrated high-speed and controllable phototactic locomotion. This autonomous robot is battery free, deployable, scalable, and integratable. Artificial phototaxis opens broad opportunities in maneuverable marine automated systems, miniaturized transportation, and solar sails
Can We Evaluate Domain Adaptation Models Without Target-Domain Labels? A Metric for Unsupervised Evaluation of Domain Adaptation
Unsupervised domain adaptation (UDA) involves adapting a model trained on a
label-rich source domain to an unlabeled target domain. However, in real-world
scenarios, the absence of target-domain labels makes it challenging to evaluate
the performance of deep models after UDA. Additionally, prevailing UDA methods
typically rely on adversarial training and self-training, which could lead to
model degeneration and negative transfer, further exacerbating the evaluation
problem. In this paper, we propose a novel metric called the \textit{Transfer
Score} to address these issues. The transfer score enables the unsupervised
evaluation of domain adaptation models by assessing the spatial uniformity of
the classifier via model parameters, as well as the transferability and
discriminability of the feature space. Based on unsupervised evaluation using
our metric, we achieve three goals: (1) selecting the most suitable UDA method
from a range of available options, (2) optimizing hyperparameters of UDA models
to prevent model degeneration, and (3) identifying the epoch at which the
adapted model performs optimally. Our work bridges the gap between UDA research
and practical UDA evaluation, enabling a realistic assessment of UDA model
performance. We validate the effectiveness of our metric through extensive
empirical studies conducted on various public datasets. The results demonstrate
the utility of the transfer score in evaluating UDA models and its potential to
enhance the overall efficacy of UDA techniques
Diffusion Glancing Transformer for Parallel Sequence to Sequence Learning
Previously, non-autoregressive models were widely perceived as being superior
in generation efficiency but inferior in generation quality due to the
difficulties of modeling multiple target modalities. To enhance the
multi-modality modeling ability, we propose the diffusion glancing transformer,
which employs a modality diffusion process and residual glancing sampling. The
modality diffusion process is a discrete process that interpolates the
multi-modal distribution along the decoding steps, and the residual glancing
sampling approach guides the model to continuously learn the remaining
modalities across the layers. Experimental results on various machine
translation and text generation benchmarks demonstrate that DIFFGLAT achieves
better generation accuracy while maintaining fast decoding speed compared with
both autoregressive and non-autoregressive models.Comment: 8 pages, 7 figure
Shunting of prostanoid biosynthesis in microsomal prostaglandin E synthase‐1 null embryo fibroblasts: regulatory effects on inducible nitric oxide synthase expression and nitrite synthesis
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154471/1/fsb2fj066366fje.pd
Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning
The recent surge of generative AI has been fueled by the generative power of
diffusion probabilistic models and the scalable capabilities of large language
models. Despite their potential, it remains elusive whether diffusion language
models can solve general language tasks comparable to their autoregressive
counterparts. This paper demonstrates that scaling diffusion models w.r.t.
data, sizes, and tasks can effectively make them strong language learners. We
build competent diffusion language models at scale by first acquiring knowledge
from massive data via masked language modeling pretraining thanks to their
intrinsic connections. We then reprogram pretrained masked language models into
diffusion language models via diffusive adaptation, wherein task-specific
finetuning and instruction finetuning are explored to unlock their versatility
in solving general language tasks. Experiments show that scaling diffusion
language models consistently improves performance across downstream language
tasks. We further discover that instruction finetuning can elicit zero-shot and
few-shot in-context learning abilities that help tackle many unseen tasks by
following natural language instructions, and show promise in advanced and
challenging abilities such as reasoning.Comment: added reference
PP2A Mediated AMPK Inhibition Promotes HSP70 Expression in Heat Shock Response
BACKGROUND: Under stress, AMP-activated protein kinase (AMPK) plays a central role in energy balance, and the heat shock response is a protective mechanism for cell survival. The relationship between AMPK activity and heat shock protein (HSP) expression under stress is unclear. METHODOLOGY/PRINCIPAL FINDINGS: We found that heat stress induced dephosphorylation of AMPKα subunit (AMPKα) in various cell types from human and rodent. In HepG2 cells, the dephosphorylation of AMPKα under heat stress in turn caused dephosphorylation of acetyl-CoA carboxylase and upregulation of phosphoenolpyruvate carboxykinase, two downstream targets of AMPK, confirming the inhibition of AMPK activity by heat stress. Treatment of HepG2 cells with phosphatase 2A (PP2A) inhibitor okadaic acid or inhibition of PP2A expression by RNA interference efficiently reversed heat stress-induced AMPKα dephosphorylation, suggesting that heat stress inhibited AMPK through activation of PP2A. Heat stress- and other HSP inducer (CdCl(2), celastrol, MG132)-induced HSP70 expression could be inhibited by AICAR, an AMPK specific activator. Inhibition of AMPKα expression by RNA interference reversed the inhibitory effect of AICAR on HSP70 expression under heat stress. These results indicate that AMPK inhibition under stress contribute to HSP70 expression. Mechanistic studies showed that activation of AMPK by AICAR had no effect on heat stress-induced HSF1 nuclear translocation, phosphorylation and binding with heat response element in the promoter region of HSP70 gene, but significantly decreased HSP70 mRNA stability. CONCLUSIONS/SIGNIFICANCE: These results demonstrate that during heat shock response, PP2A mediated AMPK inhibition upregulates HSP70 expression at least partially through stabilizing its mRNA, which suggests a novel mechanism for HSP induction under stress
Imidazole-thiazolidinone inhibits oesophageal cancer cell proliferation via induction of apoptosis and cell cycle arrest at S phase
Purpose: To investigate the effect of imidazole-thiazolidinone on oesophageal cancer (OC) cell proliferation, and the mechanism of action involved.Methods: Human OC cells (HCE-6 and KYSE-1170) were cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10 % fetal bovine serum (FBS) and 1 % penicillin/streptomycin solution at 37 ˚C for 24 h in a humidified atmosphere of 5 % CO2 and 95 % air. After attaining 60 - 70 % confluency, the cells were treated with serum-free medium and graded concentrations of imidazolethiazolidinone (up to 160 μM) for 24 h. Normal cell culture without imidazole-thiazolidinone served as control. Cells in logarithmic growth phase were selected and used in this study. Cell proliferation and apoptosis were assessed using 3 (4,5 dimethyl thiazol 2 yl) 2,5 diphenyl 2H tetrazolium bromide (MTT), and flow cytometric assays, respectively. The levels of expression of apoptosis-related proteins were determined using Western blotting.Results: Treatment of HCE-6 and KYSE-1170 cells with imidazole-thiazolidinone for 48 h led to significant and dose-dependent reduction in their proliferation, as well as significant and dosedependent increase in the number of apoptotic cells (p < 0.05). Light microscopy revealed significantreduction in HCE-6 cell count, detached cells, reduced cell size and irregular cytoplasmic vacuoles. Imidazole-thiazolidinone treatment significantly and dose-dependently decreased HCE-6 and KYSE-1170 cell migration, and arrested HCE-6 cell cycle at S phase (p < 0.05). In HCE-6 cells, imidazolethiazolidinone treatment significantly and dose-dependently upregulated the expressions of cleaved caspase-3/8/9 and bax, but down-regulated bcl-2 expression significantly and dose-dependently (p < 0.05). However, metalloproteinases 2 and 9 (MMP-2 and MMP-9) expressions in HCE-6 and KYSE-1170 cells were significantly and dose-dependently down-regulated by imidazole-thiazolidinone treatment (p < 0.05).Conclusion: The results obtained in this study suggest that imidazole-thiazolidinone suppresses OC cell proliferation via induction of apoptosis and arrest of cell cycle at S phase.
Keywords: Imidazole-thiazolidinone, Oesophageal cancer, Metastasis, Cell cycle arrest, Apoptosi
HandGCAT: Occlusion-Robust 3D Hand Mesh Reconstruction from Monocular Images
We propose a robust and accurate method for reconstructing 3D hand mesh from
monocular images. This is a very challenging problem, as hands are often
severely occluded by objects. Previous works often have disregarded 2D hand
pose information, which contains hand prior knowledge that is strongly
correlated with occluded regions. Thus, in this work, we propose a novel 3D
hand mesh reconstruction network HandGCAT, that can fully exploit hand prior as
compensation information to enhance occluded region features. Specifically, we
designed the Knowledge-Guided Graph Convolution (KGC) module and the
Cross-Attention Transformer (CAT) module. KGC extracts hand prior information
from 2D hand pose by graph convolution. CAT fuses hand prior into occluded
regions by considering their high correlation. Extensive experiments on popular
datasets with challenging hand-object occlusions, such as HO3D v2, HO3D v3, and
DexYCB demonstrate that our HandGCAT reaches state-of-the-art performance. The
code is available at https://github.com/heartStrive/HandGCAT.Comment: 6 pages, 4 figures, ICME-2023 conference pape
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