Snail-inspired robotic swarms: a hybrid connector drives collective adaptation in unstructured outdoor environments.

Terrestrial self-reconfigurable robot swarms offer adaptable solutions for various tasks. However, most existing swarms are limited to controlled indoor settings, and often compromise stability due to their freeform connections. To address these issues, we present a snail robotic swarm system inspired by land snails, tailored for unstructured environments. Our system also employs a two-mode connection mechanism, drawing from the adhesive capabilities of land snails. The free mode, mirroring a snail's natural locomotion, leverages magnet-embedded tracks for freeform mobility, thereby enhancing adaptability and efficiency. The strong mode, analogous to a snail's response to disturbance, employs a vacuum sucker with directional polymer stalks for robust adhesion. By assigning specific functions to each mode, our system achieves a balance between mobility and secure connections. Outdoor experiments demonstrate the capabilities of individual robots and the exceptional synergy within the swarm. This research advances the real-world applications of terrestrial robotic swarms in unstructured environments.

DG2GAN: improving defect recognition performance with generated defect image sample.

This article aims to improve the deep-learning-based surface defect recognition. In actual manufacturing processes, there are issues such as data imbalance, insufficient diversity, and poor quality of augmented data in the collected image data for product defect recognition. A novel defect generation method with multiple loss functions, DG2GAN is presented in this paper. This method employs cycle consistency loss to generate defect images from a large number of defect-free images, overcoming the issue of imbalanced original training data. DJS optimized discriminator loss is introduced in the added discriminator to encourage the generation of diverse defect images. Furthermore, to maintain diversity in generated images while improving image quality, a new DG2 adversarial loss is proposed with the aim of generating high-quality and diverse images. The experiments demonstrated that DG2GAN produces defect images of higher quality and greater diversity compared with other advanced generation methods. Using the DG2GAN method to augment defect data in the CrackForest and MVTec datasets, the defect recognition accuracy increased from 86.9 to 94.6%, and the precision improved from 59.8 to 80.2%. The experimental results show that using the proposed defect generation method can obtain sample images with high quality and diversity and employ this method for data augmentation significantly enhances surface defect recognition technology.

Deep Depth Completion From Extremely Sparse Data: A Survey.

Depth completion aims at predicting dense pixel-wise depth from an extremely sparse map captured from a depth sensor, e.g., LiDARs. It plays an essential role in various applications such as autonomous driving, 3D reconstruction, augmented reality, and robot navigation. Recent successes on the task have been demonstrated and dominated by deep learning based solutions. In this article, for the first time, we provide a comprehensive literature review that helps readers better grasp the research trends and clearly understand the current advances. We investigate the related studies from the design aspects of network architectures, loss functions, benchmark datasets, and learning strategies with a proposal of a novel taxonomy that categorizes existing methods. Besides, we present a quantitative comparison of model performance on three widely used benchmarks, including indoor and outdoor datasets. Finally, we discuss the challenges of prior works and provide readers with some insights for future research directions.

Lifelong-MonoDepth: Lifelong Learning for Multidomain Monocular Metric Depth Estimation.

With the rapid advancements in autonomous driving and robot navigation, there is a growing demand for lifelong learning (LL) models capable of estimating metric (absolute) depth. LL approaches potentially offer significant cost savings in terms of model training, data storage, and collection. However, the quality of RGB images and depth maps is sensor-dependent, and depth maps in the real world exhibit domain-specific characteristics, leading to variations in depth ranges. These challenges limit existing methods to LL scenarios with small domain gaps and relative depth map estimation. To facilitate lifelong metric depth learning, we identify three crucial technical challenges that require attention: 1) developing a model capable of addressing the depth scale variation through scale-aware depth learning; 2) devising an effective learning strategy to handle significant domain gaps; and 3) creating an automated solution for domain-aware depth inference in practical applications. Based on the aforementioned considerations, in this article, we present 1) a lightweight multihead framework that effectively tackles the depth scale imbalance; 2) an uncertainty-aware LL solution that adeptly handles significant domain gaps; and 3) an online domain-specific predictor selection method for real-time inference. Through extensive numerical studies, we show that the proposed method can achieve good efficiency, stability, and plasticity, leading the benchmarks by 8%-15%. The code is available at https://github.com/FreeformRobotics/Lifelong-MonoDepth.

A Novel and More Efficient Oscillating Foil for Wave-Driven Unmanned Surface Vehicles.

In the wave-driven unmanned surface vehicles (WUSVs), oscillating-foils are the most straightforward and widely used wave energy conversion mechanism, like the wave glider. However, WUSVs usually sail slowly compared with other types of USVs. Improving the thrust of the oscillating foil to increase its speed can help WUSVs improve their maneuverability and shorten the completion of ocean missions. This paper proposed a novel method to enhance oscillating foils' thrust force using asymmetric cross-section shape and asymmetric oscillating motion. The thrust enhancement effect is verified by CFD simulation and pool experiment. The experimental results show that the asymmetric wing can enhance the propulsive force by at least 13.75%. The speed enhancement of WUSVs brought by this enhanced thrust is at least 7.6%, which has also been verified by simulation and sea experiment. The asymmetric foil only needs to make low-cost modifications on the traditional rigid symmetric foil to achieve the desired thrust enhancement effect.

Toward Better Accuracy-Efficiency Trade-Offs: Divide and Co-Training.

The width of a neural network matters since increasing the width will necessarily increase the model capacity. However, the performance of a network does not improve linearly with the width and soon gets saturated. In this case, we argue that increasing the number of networks (ensemble) can achieve better accuracy-efficiency trade-offs than purely increasing the width. To prove it, one large network is divided into several small ones regarding its parameters and regularization components. Each of these small networks has a fraction of the original one's parameters. We then train these small networks together and make them see various views of the same data to increase their diversity. During this co-training process, networks can also learn from each other. As a result, small networks can achieve better ensemble performance than the large one with few or no extra parameters or FLOPs, i. e., achieving better accuracy-efficiency trade-offs. Small networks can also achieve faster inference speed than the large one by concurrent running. All of the above shows that the number of networks is a new dimension of model scaling. We validate our argument with 8 different neural architectures on common benchmarks through extensive experiments.

Pegylated derivatives of recombinant human arginase (rhArg1) for sustained in vivo activity in cancer therapy: preparation, characterization and analysis of their pharmacodynamics in vivo and in vitro and action upon hepatocellular carcinoma cell (HCC).

BACKGROUND: Protein used in medicine, e.g. interferon, are immunogenic and quickly broken down by the body. Pegylation is a recognized way of preserving their integrity and reducing immune reactions, and works well with enzymes used to degrade amino acids, a recent focus of attention in controlling cancer growth. Of the two arginine-degrading enzymes being explored clinically, arginine deiminase is a decidedly foreign mycoplasm-derived enzyme, whereas human arginase 1 is a native liver enzyme. Both have been pegylated, the former with adjuncts of 20 kD, the latter with 5 kD PEG. Pegylation is done by several different methods, not all of which are satisfactory or desirable. METHODS: The preparation of novel polyethylene glycol (PEG) derivatives for modifying proteins is described, but directed specifically at pegylation of recombinant human arginase 1 (rhArg1). rhArg1 expressed in Escherichia coli was purified and coupled in various ways with 5 different PEG molecules to compare their protective properties and the residual enzyme activity, using hepatocellular cell lines both in vitro and in vivo. RESULTS: Methoxypolyethylene glycol-succinimidyl propionate (mPEG-SPA 5,000) coupled with very high affinity under mild conditions. The resulting pegylated enzyme (rhArg1-peg5,000 mw) had up to 6 PEG chains of 5K length which not only protected it from degradation and any residual immunogenicity, but most importantly let it retain >90% of its native catalytic activity. It remained efficacious in depleting arginine in rats after a single ip injection of 1,500 U of the conjugate as the native enzyme, plasma arginine falling to >0.05 microM from approximately 170 microM within 20 min and lasting 6 days. The conjugate had almost the same efficacy as unpegylated rhArg1 on 2 cultured human liver cancer (HCC) cell lines. It was considerably more effective than 4 other pegylated conjugates prepared. CONCLUSION: Valuable data on the optimization of the pegylation procedure and choice of ligand that best stabilizes the enzyme arginase 1 are presented, a protocol that should equally fit many other enzymes and proteins. It is a long lasting arginine-depleting enzyme in vivo which will greatly improve its use in anti-cancer therapy.

Pegylated recombinant human arginase (rhArg-peg5,000mw) inhibits the in vitro and in vivo proliferation of human hepatocellular carcinoma through arginine depletion.

Hepatocellular carcinoma (HCC) is believed to be auxotrophic for arginine through the lack of expression of argininosuccinate synthetase (ASS). The successful use of the arginine-depleting enzyme arginine deiminase (ADI) to treat ASS-deficient tumors has opened up new possibilities for effective cancer therapy. Nevertheless, many ASS-positive HCC cell lines are found to be resistant to ADI treatment, although most require arginine for proliferation. Thus far, an arginine-depleting enzyme for killing ASS-positive tumors has not been reported. Here, we provide direct evidence that recombinant human arginase (rhArg) inhibits ASS-positive HCCs. All the five human HCC cell lines we used were sensitive to rhArg but ADI had virtually no effect on these cells. They all expressed ASS, but not ornithine transcarbamylase (OTC), the enzyme that converts ornithine, the product of degradation of arginine with rhArg, to citrulline, which is converted back to arginine via ASS. Transfection of HCC cells with OTC resulted in resistance to rhArg. Thus, OTC expression alone may be sufficient to induce rhArg resistance in ASS-positive HCC cells. This surprising correlation between the lack of OTC expression and sensitivity of ASS-positive HCC cells shows that OTC-deficient HCCs are sensitive to rhArg-mediated arginine depletion. Therefore, pretreatment tumor gene expression profiling of ASS and OTC could aid in predicting tumor response to arginine depletion with arginine-depleting enzymes. We have also shown that the rhArg native enzyme and the pegylated rhArg (rhArg-peg(5,000mw)) gave similar anticancer efficacy in vitro. Furthermore, the growth of the OTC-deficient Hep3B tumor cells (ASS-positive and ADI-resistant) in mice was inhibited by treatment with rhArg-peg(5,000mw), which is active alone and is synergistic in combination with 5-fluorouracil. Thus, our data suggest that rhArg-peg(5,000mw) is a novel agent for effective cancer therapy.

Monitoring of dopamine release in single cell using ultrasensitive ITO microsensors modified with carbon nanotubes.

The study of single cell dynamics has been greatly adapted in biological and medical research and applications. In this work a novel microfluidic electrochemical sensor with carbon nanotubes (CNTs) modified indium tin oxide (ITO) microelectrode was developed for single cells release monitoring. The sensitivity of the electrochemical sensor after CNTs surface modification was improved by 2.5-3 orders of magnitude. The developed CNTs modified ITO sensor was successfully employed to monitor the dopamine release from single living rat pheochromocytoma (PC 12) cells. Its ultrahigh sensitivity, transparency and need for fewer agents enable this smart electrochemical sensor to become a powerful tool in recording dynamic release from various living tissues and organs optically and electrically.

Recombinant human arginase inhibits the in vitro and in vivo proliferation of human melanoma by inducing cell cycle arrest and apoptosis.

Melanoma has been shown to require arginine for growth, thus providing a potential Achilles' heel for therapeutic exploitation. Our investigations show that arginine depletion, using a recombinant form of human arginase I (rhArg), efficiently inhibits the growth of mammalian melanoma cell lines in vitro. These cell lines are consistently deficient in ornithine transcarbamylase (OTC) expression, correlating with their sensitivity to rhArg. Cell cycle distribution of A375 human melanoma cells treated with rhArg showed a remarkable dual-phase cell cycle arrest in S and G2/M phases, in contrast to the G2/M single-phase arrest observed with arginine deiminase (ADI), another arginine-degrading enzyme. rhArg and ADI both induced substantial apoptosis in A375 cells, accompanied by global modulation of cell cycle- and apoptosis-related transcription. Moreover, PEGylated rhArg dramatically inhibited the growth of A375 and B16 melanoma xenografts in vivo. Our results establish for the first time that (PEGylated) rhArg is a promising candidate for effective melanoma treatment, with fewer safety issues than ADI. Insight into the mechanism behind the antiproliferative activity of rhArg could inform us in designing combination therapies for future clinical trials.

Release monitoring of single cells on a microfluidic device coupled with fluorescence microscopy and electrochemistry.

A method for monitoring the biological exocytotic phenomena on a microfluidic system was proposed. A microfluidic device coupled with functionalities of fluorescence imaging and amperometric detection has been developed to enable the real-time monitoring of the exocytotic events. Exocytotic release of single SH-SY5Y neuroblastoma cells was studied. By staining the cells located on integrated microelectrodes with naphthalene-2,3-dicarboxaldehyde, punctuate fluorescence consistent with localization of neurotransmitters stored in vesicles was obtained. The stimulated exocytotic release was successfully observed at the surface of SH-SY5Y cells without refitting the commercial inverted fluorescence microscope. Spatially and temporally resolved exocytotic events from single cells on a microfluidic device were visualized in real time using fluorescence microscopy and were amperometrically recorded by the electrochemical system simultaneously. This coupled technique is simple and is hoped to provide new insights into the mechanisms responsible for the kinetics of exocytosis.

A microfluidic system with surface modified piezoelectric sensor for trapping and detection of cancer cells.

We present a microfluidic system with integrated surface modified piezoelectric sensor for trapping and label-free detection of target samples that can be used for cancer diagnosis. To introduce active magnetic force control to the piezoelectric sensor and obtain a fast regenerative system, a nickel pillar array was patterned onto the surface of quartz crystal microbalance. The functionalities of the system were tested by trapping suspended superparamagnetic micro-beads (SPMBs) in fluids onto the nickel pillar array with a manually controlled magnetic field and by detecting the accumulated mass from the resonant characterization of the piezoelectric sensor at the same time. The results showed the efficiency of the system in manipulating the SPMBs and the sensitivity of the device was of 5 Hz mm2/ng. With surface-functionalized SPMBs, the microfluidic system succeeded in trapping and detecting target cancer cells. A549 cancer cells were captured on the nickel pillar array with WGA protein-functionalized SPMBs and detected with a total mass of 90.6 ng. With its simplicity, low production cost and high efficiency, this new device configuration would be useful for the future point-of-care clinical applications.