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

MixNN: A design for protecting deep learning models

In this paper, we propose a novel design, called MixNN, for protecting deep learning model structure and parameters. The layers in a deep learning model of MixNN are fully decentralized. It hides communication address, layer parameters and operations, and forward as well as backward message flows among non-adjacent layers using the ideas from mix networks. MixNN has following advantages: 1) an adversary cannot fully control all layers of a model including the structure and parameters, 2) even some layers may collude but they cannot tamper with other honest layers, 3) model privacy is preserved in the training phase. We provide detailed descriptions for deployment. In one classification experiment, we compared a neural network deployed in a virtual machine with the same one using the MixNN design on the AWS EC2. The result shows that our MixNN retains less than 0.001 difference in terms of classification accuracy, while the whole running time of MixNN is about 7.5 times slower than the one running on a single virtual machine

Mutual Information Learned Regressor: an Information-theoretic Viewpoint of Training Regression Systems

As one of the central tasks in machine learning, regression finds lots of applications in different fields. An existing common practice for solving regression problems is the mean square error (MSE) minimization approach or its regularized variants which require prior knowledge about the models. Recently, Yi et al., proposed a mutual information based supervised learning framework where they introduced a label entropy regularization which does not require any prior knowledge. When applied to classification tasks and solved via a stochastic gradient descent (SGD) optimization algorithm, their approach achieved significant improvement over the commonly used cross entropy loss and its variants. However, they did not provide a theoretical convergence analysis of the SGD algorithm for the proposed formulation. Besides, applying the framework to regression tasks is nontrivial due to the potentially infinite support set of the label. In this paper, we investigate the regression under the mutual information based supervised learning framework. We first argue that the MSE minimization approach is equivalent to a conditional entropy learning problem, and then propose a mutual information learning formulation for solving regression problems by using a reparameterization technique. For the proposed formulation, we give the convergence analysis of the SGD algorithm for solving it in practice. Finally, we consider a multi-output regression data model where we derive the generalization performance lower bound in terms of the mutual information associated with the underlying data distribution. The result shows that the high dimensionality can be a bless instead of a curse, which is controlled by a threshold. We hope our work will serve as a good starting point for further research on the mutual information based regression.Comment: 28 pages, 2 figures, presubmitted to AISTATS2023 for reviewin

NPC-EXs Alleviate Endothelial Oxidative Stress and Dysfunction through the miR-210 Downstream Nox2 and VEGFR2 Pathways

We have demonstrated that neural progenitor cells (NPCs) protect endothelial cells (ECs) from oxidative stress. Since exosomes (EXs) can convey the benefit of parent cells through their carried microRNAs (miRs) and miR-210 is ubiquitously expressed with versatile functions, we investigated the role of miR-210 in the effects of NPC-EXs on oxidative stress and dysfunction in ECs. NPCs were transfected with control and miR-210 scramble/inhibitor/mimic to generate NPC-EXscon, NPC-EXssc, NPC-EXsanti-miR-210, and NPC-EXsmiR-210. The effects of various NPC-EXs on angiotensin II- (Ang II-) induced reactive oxygen species (ROS) overproduction, apoptosis, and dysfunction, as well as dysregulation of Nox2, ephrin A3, VEGF, and p-VEGFR2/VEGFR2 in ECs were evaluated. Results showed (1) Ang II-induced ROS overproduction, increase in apoptosis, and decrease in tube formation ability, accompanied with Nox2 upregulation and reduction of p-VEGFR2/VEGFR2 in ECs. (2) Compared to NPC-EXscon or NPC-EXssc, NPC-EXsanti-miR-210 were less whereas NPC-EXsmiR-210 were more effective on attenuating these detrimental effects induced by Ang II in ECs. (3) These effects of NPC-EXsanti-miR-210 and NPC-EXsmiR-210 were associated with the changes of miR-210, ephrin A3, VEGF, and p-VEGFR2/VEGFR2 ratio in ECs. Altogether, the protective effects of NPC-EXs on Ang II-induced endothelial injury through miR-210 which controls Nox2/ROS and VEGF/VEGFR2 signals were studied

Keratin 18 phosphorylation as a progression marker of chronic hepatitis B

BACKGROUND: The intermediate filament proteins keratins 18 (K18) and 8 (K8) polymerize to form the cytoskeletal network in the mature hepatocytes. It has been shown that the phosphorylation of K18 at two serine residues, 33 and 52, correlates with the progression of hepatitis C, but little is known of chronic hepatitis B (CHB). In this study, we examined K18 phosphorylation in relation to CHB. RESULTS: Site-specific phosphorylation of K18 was determined in livers of twelve healthy donors, and non-cirrhosis (n = 40) and cirrhosis (n = 21) patients. On average, progressively higher level of Ser52 phosphorylation was observed in non-cirrhotic and cirrhotic livers, while elevated Ser33 phosphorylation was detected in both livers but no significant difference. Progressive increase of Ser33 and Ser52 phosphorylation correlated with the elevation of both histological lesions and enzymatic activities of alanine aminotransferase in non-cirrhotic livers. In the hepatocytes of an inactive HBV carrier, strong signals of Ser33 phosphorylation were co-localized with viral infection, while only basal level of Ser52 phosphorylation was detected in infected cells. CONCLUSION: Assuming all obtained data, our data suggest that K18 phosphorylation is a progression marker for CHB