Mechanical compression to characterize the robustness of liquid marbles

In this work, we have devised a new approach to measure the critical pressure that a liquid marble can withstand. A liquid marble is gradually squeezed under a mechanical compression applied by two parallel plates. It ruptures at a sufficiently large applied pressure. Combining the force measurement and the high-speed imaging, we can determine the critical pressure that ruptures the liquid marble. This critical pressure, which reflects the mechanical robustness of liquid marbles, depends on the type and size of the stabilizing particles as well as the chemical nature of the liquid droplet. By investigating the surface of the liquid marble, we attribute its rupture under the critical pressure to the low surface coverage of particles when highly stretched. Moreover, the applied pressure can be reflected by the inner Laplace pressure of the liquid marble considering the squeezing test is a quasi-static process. By analyzing the Laplace pressure upon rupture of the liquid marble, we predict the dependence of the critical pressure on the size of the liquid marble, which agrees well with experimental results

Coalescence of electrically charged liquid marbles

© The Royal Society of Chemistry. In this work, we investigated the coalescence of liquid water marbles driven by a DC electric field. We have found that two contacting liquid marbles can be forced to coalesce when they are charged by a sufficiently high voltage. The threshold voltage leading to the electro-coalescence sensitively depends on the stabilizing particles as well as the surface tension of the aqueous phase. By evaluating the electric stress and surface tension effect, we attribute such coalescence to the formation of a connecting bridge driven by the electric stress. This liquid bridge subsequently grows and leads to the merging of the marbles. Our interpretation is confirmed by the scaling relation between the electric stress and the restoring capillary pressure. In addition, multiple marbles in a chain can be driven to coalesce by a sufficiently high threshold voltage that increases linearly with the number of the marbles. We have further proposed a simple model to predict the relationship between the threshold voltage and the number of liquid marbles, which agrees well with the experimental results. The concept of electro-coalescence of liquid marbles can be potentially useful in their use as containers for chemical and biomedical reactions involving multiple reagents

DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration

We present DeepICP - a novel end-to-end learning-based 3D point cloud registration framework that achieves comparable registration accuracy to prior state-of-the-art geometric methods. Different from other keypoint based methods where a RANSAC procedure is usually needed, we implement the use of various deep neural network structures to establish an end-to-end trainable network. Our keypoint detector is trained through this end-to-end structure and enables the system to avoid the inference of dynamic objects, leverages the help of sufficiently salient features on stationary objects, and as a result, achieves high robustness. Rather than searching the corresponding points among existing points, the key contribution is that we innovatively generate them based on learned matching probabilities among a group of candidates, which can boost the registration accuracy. Our loss function incorporates both the local similarity and the global geometric constraints to ensure all above network designs can converge towards the right direction. We comprehensively validate the effectiveness of our approach using both the KITTI dataset and the Apollo-SouthBay dataset. Results demonstrate that our method achieves comparable or better performance than the state-of-the-art geometry-based methods. Detailed ablation and visualization analysis are included to further illustrate the behavior and insights of our network. The low registration error and high robustness of our method makes it attractive for substantial applications relying on the point cloud registration task.Comment: 10 pages, 6 figures, 3 tables, typos corrected, experimental results updated, accepted by ICCV 201

Learning Meta Model for Zero- and Few-shot Face Anti-spoofing

Face anti-spoofing is crucial to the security of face recognition systems. Most previous methods formulate face anti-spoofing as a supervised learning problem to detect various predefined presentation attacks, which need large scale training data to cover as many attacks as possible. However, the trained model is easy to overfit several common attacks and is still vulnerable to unseen attacks. To overcome this challenge, the detector should: 1) learn discriminative features that can generalize to unseen spoofing types from predefined presentation attacks; 2) quickly adapt to new spoofing types by learning from both the predefined attacks and a few examples of the new spoofing types. Therefore, we define face anti-spoofing as a zero- and few-shot learning problem. In this paper, we propose a novel Adaptive Inner-update Meta Face Anti-Spoofing (AIM-FAS) method to tackle this problem through meta-learning. Specifically, AIM-FAS trains a meta-learner focusing on the task of detecting unseen spoofing types by learning from predefined living and spoofing faces and a few examples of new attacks. To assess the proposed approach, we propose several benchmarks for zero- and few-shot FAS. Experiments show its superior performances on the presented benchmarks to existing methods in existing zero-shot FAS protocols.Comment: Accepted by AAAI202

Controlled actuation of liquid marbles on a dielectric

Motivated by the great potential of droplet microreactors for chemical and biological applications, a general and robust method utilizing an electric field is developed for sustained, directional and two-dimensional manipulation of nonwetting droplets (termed “liquid marbles”). With the understanding of the mechanism of actuation, this method allows individual liquid marbles to be actuated and coalesced on demand by fine-tuning the driving voltage. Moreover, in our system, cross-contamination between marbles during manipulation is avoided as confirmed by the absence of any trace DNA after amplification using a loop-mediated isothermal amplification reaction

Identification of a Novel Tumor Microenvironment–Associated Eight-Gene Signature for Prognosis Prediction in Lung Adenocarcinoma

Background: Lung cancer has become the most common cancer type and caused the most cancer deaths. Lung adenocarcinoma (LUAD) is one of the major types of lung cancer. Accumulating evidence suggests the tumor microenvironment is correlated with the tumor progress and the patient's outcome. This study aimed to establish a gene signature based on tumor microenvironment that can predict patients' outcomes for LUAD. Methods: Dataset TCGA-LUAD, downloaded from the TCGA portal, were taken as training cohort, and dataset GSE72094, obtained from the GEO database, was set as validation cohort. In the training cohort, ESTIMATE algorithm was applied to find intersection differentially expressed genes (DEGs) among tumor microenvironment. Kaplan-Meier analysis and univariate Cox regression model were performed on intersection DEGs to preliminarily screen prognostic genes. Besides, the LASSO Cox regression model was implemented to build a multi-gene signature, which was then validated in the validation cohorts through Kaplan-Meier, Cox, and receiver operating characteristic curve (ROC) analyses. In addition, the correlation between tumor mutational burden (TMB) and risk score was evaluated by Spearman test. GSEA and immune infiltrating analyses were conducted for understanding function annotation and the role of the signature in the tumor microenvironment. Results: An eight-gene signature was built, and it was examined by Kaplan-Meier analysis, revealing that a significant overall survival difference was seen. The eight-gene signature was further proven to be independent of other clinico-pathologic parameters via the Cox regression analyses. Moreover, the ROC analysis demonstrated that this signature owned a better predictive power of LUAD prognosis. The eight-gene signature was correlated with TMB. Furthermore, GSEA and immune infiltrating analyses showed that the exact pathways related to the characteristics of eight-genes signature, and identified the vital roles of Mast cells resting and B cells naive in the prognosis of the eight-gene signature. Conclusion: Identifying the eight-gene signature (INSL4, SCN7A, STAP1, P2RX1, IKZF3, MS4A1, KLRB1, and ACSM5) could accurately identify patients' prognosis and had close interactions with Mast cells resting and B cells naive, which may provide insight into personalized prognosis prediction and new therapies for LUAD patients

Electrocoalescence of liquid marbles driven by embedded electrodes for triggering bioreactions

Liquid marbles need to be controlled precisely to benefit applications, for instance, as microreactors on digital microfluidic platforms for chemical and biological assays. In this work, a strategy is introduced to coalesce liquid marbles via electrostatics, where two liquid marbles in contact can coalesce when a sufficiently high voltage is applied to embedded electrodes. With the understanding of the mechanism of coalescence through relating the electric stress and the restoring capillary pressure at the contact interface, this method coalesces liquid marbles efficiently. When compared with the existing electrocoalescence method, our approach does not require immersion of electrodes to trigger coalescence. We demonstrate this to exchange the medium for the culture of cell spheroids and to measure the cell metabolic activity through a CCK-8 assay. The manipulation of liquid marbles driven by electrostatics creates new opportunities to conduct chemical reactions and biomedical assays in these novel microreactors