miR-144-3p represses hepatocellular carcinoma progression by affecting cell aerobic glycolysis via FOXK1.

Aerobic glycolysis is a unique mark of cancer cells, which enables therapeutic intervention in cancer. Forkhead box K1 (FOXK1) is a transcription factor that facilitates the progression of multiple cancers including hepatocellular carcinoma (HCC). Nevertheless, it is unclear whether or not FOXK1 can affect HCC cell glycolysis. This study attempted to study the effect of FOXK1 on HCC cell glycolysis. Expression of mature miRNAs and mRNAs, as well as clinical data, was downloaded from The Cancer Genome Atlas-Liver hepatocellular carcinoma (TCGA-LIHC) dataset. FOXK1 and miR-144-3p levels were assessed through quantitative real-time polymerase chain reaction (qRT-PCR) and western blot. Targeting of the relationship between miR-144-3p and FOXK1 was verified via a dual-luciferase assay. Pathway enrichment analysis of FOXK1 was performed by Gene Set Enrichment Analysis (GSEA). Cell function assays revealed the glycolytic ability, cell viability, migration, invasion, cell cycle, and apoptosis of HCC cells in each treatment group. Bioinformatics analysis suggested that FOXK1 was upregulated in tissues of HCC patients, while the upstream miR-144-3p was downregulated in tumour tissues. Dual-luciferase assay implied a targeting relationship between miR-144-3p and FOXK1. Cellular experiments implied that silencing FOXK1 repressed HCC cell glycolysis, which in turn inhibited the HCC malignant progression. Rescue assay confirmed that miR-144-3p repressed glycolysis in HCC cells by targeting FOXK1, and then repressed HCC malignant progression. miR-144-3p/FOXK1 axis repressed malignant progression of HCC via affecting the aerobic glycolytic process of HCC cells. miR-144-3p and FOXK1 have the potential to become new therapeutic targets for HCC, which provide new insights for HCC treatment.

Macrophage migration inhibitory factor MtMIF3 prevents the premature aging of Medicago truncatula nodules.

Macrophage migration inhibitory factor (MIF) is a proinflammatory cytokine involved in immune response in animals. However, the role of MIFs in plants such as Medicago truncatula, particularly in symbiotic nitrogen fixation, remains unclear. An investigation of M. truncatula-Sinorhizobium meliloti symbiosis revealed that MtMIF3 was mainly expressed in the nitrogen-fixing zone of the nodules. Silencing MtMIF3 using RNA interference (Ri) technology resulted in increased nodule numbers but higher levels of bacteroid degradation in the infected cells of the nitrogen-fixing zone, suggesting that premature aging was induced in MtMIF3-Ri nodules. In agreement with this conclusion, the activities of nitrogenase, superoxide dismutase and catalase were lower than those in controls, but cysteine proteinase activity was increased in nodulated roots at 28 days postinoculation. In contrast, the overexpression of MtMIF3 inhibited nodule senescence. MtMIF3 is localized in the plasma membrane, nucleus, and cytoplasm, where it interacts with methionine sulfoxide reductase B (MsrB), which is also localized in the chloroplasts of tobacco leaf cells. Taken together, these results suggest that MtMIF3 prevents premature nodule aging and protects against oxidation by interacting with MtMsrB.

Radiomics under 2D regions, 3D regions, and peritumoral regions reveal tumor heterogeneity in non-small cell lung cancer: a multicenter study.

PURPOSE: Lung cancer has significant genetic and phenotypic heterogeneity, leading to poor prognosis. Radiomic features have emerged as promising predictors of the tumor phenotype. However, the role of underlying information surrounding the cancer remains unclear. MATERIALS AND METHODS: We conducted a retrospective study of 508 patients with NSCLC from three institutions. Radiomics models were built using features from six tumor regions and seven classifiers to predict three prognostically significant tumor phenotypes. The models were evaluated and interpreted by the mean area under the receiver operating characteristic curve (AUC) under nested cross-validation and Shapley values. The best-performing predictive models corresponding to six tumor regions and three tumor phenotypes were identified for further comparative analysis. In addition, we designed five experiments with different voxel spacing to assess the sensitivity of the experimental results to the spatial resolution of the voxels. RESULTS: Our results demonstrated that models based on 2D, 3D, and peritumoral region features yielded mean AUCs and 95% confidence intervals of 0.759 and [0.747-0.771] for lymphovascular invasion, 0.889 and [0.882-0.896] for pleural invasion, and 0.839 and [0.829-0.849] for T-staging in the testing cohort, which was significantly higher than all other models. Similar results were obtained for the model combining the three regional features at five voxel spacings. CONCLUSION: Our study revealed the predictive role of the developed methods with multi-regional features for the preoperative assessment of prognostic factors in NSCLC. The analysis of different voxel spacing and model interpretability strengthens the experimental findings and contributes to understanding the biological significance of the radiological phenotype.

Development and challenges of smart actuators based on water-responsive materials.

Water-responsive (WR) materials, due to their controllable mechanical response to humidity without energy actuation, have attracted lots of attention to the development of smart actuators. WR material-based smart actuators can transform natural humidity to a required mechanical motion and have been widely used in various fields, such as soft robots, micro-generators, smart building materials, and textiles. In this paper, the development of smart actuators based on different WR materials has been reviewed systematically. First, the properties of different biological WR materials and the corresponding actuators are summarized, including plant materials, animal materials, and microorganism materials. Additionally, various synthetic WR materials and their related applications in smart actuators have also been introduced in detail, including hydrophilic polymers, graphene oxide, carbon nanotubes, and other synthetic materials. Finally, the challenges of the WR actuator are analyzed from the three perspectives of actuator design, control methods, and compatibility, and the potential solutions are also discussed. This paper may be useful for the development of not only soft actuators that are based on WR materials, but also smart materials applied to renewable energy.

A hybrid approach based on deep learning and level set formulation for liver segmentation in CT images.

Accurate liver segmentation is essential for radiation therapy planning of hepatocellular carcinoma and absorbed dose calculation. However, liver segmentation is a challenging task due to the anatomical variability in both shape and size and the low contrast between liver and its surrounding organs. Thus we propose a convolutional neural network (CNN) for automated liver segmentation. In our method, fractional differential enhancement is firstly applied for preprocessing. Subsequently, an initial liver segmentation is obtained by using a CNN. Finally, accurate liver segmentation is achieved by the evolution of an active contour model. Experimental results show that the proposed method outperforms existing methods. One hundred fifty CT scans are evaluated for the experiment. For liver segmentation, Dice of 95.8%, true positive rate of 95.1%, positive predictive value of 93.2%, and volume difference of 7% are calculated. In addition, the values of these evaluation measures show that the proposed method is able to provide a precise and robust segmentation estimate, which can also assist the manual liver segmentation task.

Automated vessel segmentation in lung CT and CTA images via deep neural networks.

BACKGROUND: The distribution of pulmonary vessels in computed tomography (CT) and computed tomography angiography (CTA) images of lung is important for diagnosing disease, formulating surgical plans and pulmonary research. PURPOSE: Based on the pulmonary vascular segmentation task of International Symposium on Image Computing and Digital Medicine 2020 challenge, this paper reviews 12 different pulmonary vascular segmentation algorithms of lung CT and CTA images and then objectively evaluates and compares their performances. METHODS: First, we present the annotated reference dataset of lung CT and CTA images. A subset of the dataset consisting 7,307 slices for training and 3,888 slices for testing was made available for participants. Second, by analyzing the performance comparison of different convolutional neural networks from 12 different institutions for pulmonary vascular segmentation, the reasons for some defects and improvements are summarized. The models are mainly based on U-Net, Attention, GAN, and multi-scale fusion network. The performance is measured in terms of Dice coefficient, over segmentation rate and under segmentation rate. Finally, we discuss several proposed methods to improve the pulmonary vessel segmentation results using deep neural networks. RESULTS: By comparing with the annotated ground truth from both lung CT and CTA images, most of 12 deep neural network algorithms do an admirable job in pulmonary vascular extraction and segmentation with the dice coefficients ranging from 0.70 to 0.85. The dice coefficients for the top three algorithms are about 0.80. CONCLUSIONS: Study results show that integrating methods that consider spatial information, fuse multi-scale feature map, or have an excellent post-processing to deep neural network training and optimization process are significant for further improving the accuracy of pulmonary vascular segmentation.

Analysis of segmentation of lung parenchyma based on deep learning methods.

Precise segmentation of lung parenchyma is essential for effective analysis of the lung. Due to the obvious contrast and large regional area compared to other tissues in the chest, lung tissue is less difficult to segment. Special attention to details of lung segmentation is also needed. To improve the quality and speed of segmentation of lung parenchyma based on computed tomography (CT) or computed tomography angiography (CTA) images, the 4th International Symposium on Image Computing and Digital Medicine (ISICDM 2020) provides interesting and valuable research ideas and approaches. For the work of lung parenchyma segmentation, 9 of the 12 participating teams used the U-Net network or its modified forms, and others used the methods to improve the segmentation accuracy include attention mechanism, multi-scale feature information fusion. Among them, U-Net achieves the best results including that the final dice coefficient of CT segmentation is 0.991 and the final dice coefficient of CTA segmentation is 0.984. In addition, attention U-Net and nnU-Net network also performs well. In this paper, the methods chosen by 12 teams from different research groups are evaluated and their segmentation results are analyzed for the study and references to those involved.

EEG-Based Anxious States Classification Using Affective BCI-Based Closed Neurofeedback System.

PURPOSE: Anxiety disorder is one of the psychiatric disorders that involves extreme fear or worry, which can change the balance of chemicals in the brain. To the best of our knowledge, the evaluation of anxiety state is still based on some subjective questionnaires and there is no objective standard assessment yet. Unlike other methods, our approach focuses on study the neural changes to identify and classify the anxiety state using electroencephalography (EEG) signals. METHODS: We designed a closed neurofeedback experiment that contains three experimental stages to adjust subjects' mental state. The EEG resting state signal was recorded from thirty-four subjects in the first and third stages while EEG-based mindfulness recording was recorded in the second stage. At the end of each stage, the subjects were asked to fill a Visual Analogue Scale (VAS). According to their VAS score, the subjects were classified into three groups: non-anxiety, moderate or severe anxiety groups. RESULTS: After processing the EEG data of each group, support vector machine (SVM) classifiers were able to classify and identify two mental states (non-anxiety and anxiety) using the Power Spectral Density (PSD) as patterns. The highest classification accuracies using Gaussian kernel function and polynomial kernel function are 92.48 ±  1.20% and 88.60  ±  1.32%, respectively. The highest average of the classification accuracies for healthy subjects is 95.31 ±  1.97% and for anxiety subjects is 87.18 ±  3.51%. CONCLUSIONS: The results suggest that our proposed EEG neurofeedback-based classification approach is efficient for developing affective BCI system for detection and evaluation of anxiety disorder states.

Flexible all-solid-state supercapacitors of polyaniline nanowire arrays deposited on electrospun carbon nanofibers decorated with MOFs.

Porous carbons derived from metal-organic frameworks (MOFs) are promising materials for a number of energy- and environment-related applications. To integrate the powder MOFs-derived carbon into feasible engineered materials, a facile strategy to fabricate integrated flexible film is developed by growing MOFs nanoparticles on polyimide electrospun nanofibers, followed by calcination, to fabricate freestanding carbon nanofiber membranes decorated with porous carbon. Then vertically polyaniline nanowire arrays are uniformly deposited on the hierarchical porous carbon substrates by in situ polymerization. Thanks to the good distribution of MOFs-derived porous carbon on carbon nanofibers and the compact configuration interwoven by conducting polymers, the designed hybrid electrode could be used directly as a freestanding electrode for supercapacitors, which displayed a high specific capacitance of 1268 F g-1. The assembled flexible solid-state supercapacitor based on the integrated electrodes demonstrated a high volumetric capacitance of 1973 mF cm-3 and a good capacitance retention of 84.9% after 10 000 cycles, which could power a commercial light emitting diode. This strategy may shed light on the design of MOFs-based flexible materials for practical applications of supercapacitors and other electrochemical devices.

[A multi-label fusion based level set method for multiple sclerosis lesion segmentation].

A multi-label based level set model for multiple sclerosis lesion segmentation is proposed based on the shape, position and other information of lesions from magnetic resonance image. First, fuzzy c-means model is applied to extract the initial lesion region. Second, an intensity prior information term and a label fusion term are constructed using intensity information of the initial lesion region, the above two terms are integrated into a region-based level set model. The final lesion segmentation is achieved by evolving the level set contour. The experimental results show that the proposed method can accurately and robustly extract brain lesions from magnetic resonance images. The proposed method helps to reduce the work of radiologists significantly, which is useful in clinical application.

[Development of Motion Unit of Simulated Intelligent Endotracheal Suctioning Robot].

A motion unit for sucking robot with a stable motion, convenient operation and process simulation is introduced. The key parameters and process data of the sucking operation were obtained from the clinical work, which provided the basis for the design of the sucking robot motion unit. According to the points of sucking action, robotic thumb, forefinger and metacarpophalangeal joints were used to grip the suction tube, and the servo and arm structure were used to simulate the motion of the wrist and elbow to complete the rotation and push of the sputum suction tube. The feasibility is verified through the advanced sputum suction training model. The movement unit is stable in movement, and can smoothly complete the clamping, feeding, back off protection and rotating tube removal of the sputum suction tube, so as to achieve effective sputum suction.

[Development of Biliary Contrast Agents Remote Pushing Device].

A biliary contrast agents pushing device, including a syringe pushing system and a remote controller is introduced. The syringe pushing system comprises an injector card slot, a support platform and an injection bolus fader. A 20 mL syringe can be fitted on the syringe pushing system and kept with the ground about 30 degree. This system can perform air bubble pumping back and contrast agents bolus injection as well as speed adjustment. Remote controller is an infrared remote control which can start and stop the syringe pushing system. With this device, the remote controlled cholangiography technology can be achieved, which can not only protect doctors from X-ray radiation but also improve the traditional T-tube cholangiography and the contrast effect, reduce postoperative complications in patients as well. The application of this device will improve the current diagnosis and treatment system, the device will benefit the majority of doctors and patients.

Self-supervised neural network-based endoscopic monocular 3D reconstruction method.

Based on deep learning, monocular visual 3D reconstruction methods have been applied in various conventional fields. In the aspect of medical endoscopic imaging, due to the difficulty in obtaining real information, self-supervised deep learning has always been a focus of research. However, current research on endoscopic 3D reconstruction is mainly conducted in laboratory environments, lacking experience in dealing with complex clinical surgical environments. In this work, we use an optical flow-based neural network to address the problem of inconsistent brightness between frames. Additionally, attention modules and inter-layer losses are introduced to tackle the complexity of endoscopic scenes in clinical surgeries. The attention mechanism allows the network to better focus on pixel texture details and depth differences, while the inter-layer losses supervise the network at different scales. We have established a complete monocular endoscopic 3D reconstruction framework and conducted quantitative experiments on a clinical dataset using the cross-correlation coefficient as a metric. Compared with other self-supervised methods, our framework can better simulate the mapping relationship between adjacent frames during endoscope motion. To validate the generalization performance of our framework, we tested the model trained on the clinical dataset on the SCARED dataset and achieved equally excellent results.

Hierarchical classification of early microscopic lung nodule based on cascade network.

Purpose: Early-stage lung cancer is typically characterized clinically by the presence of isolated lung nodules. Thousands of cases are examined each year, and one case usually contains numerous lung CT slices. Detecting and classifying early microscopic lung nodules is demanding due to their diminutive dimensions and restricted characterization capabilities. Therefore, a lung nodule classification model that performs well and is sensitive to microscopic lung nodules is needed to accurately classify lung nodules. Methods: This paper uses the Resnet34 network as a basic classification model. A new cascade lung nodule classification method is proposed to classify lung nodules into 6 classes instead of the traditional 2 or 4 classes. It can effectively classify six different nodule types including ground-glass and solid nodules, benign and malignant nodules, and nodules with predominantly ground-glass or solid components. Results: In this paper, the traditional multi-classification method and the cascade classification method proposed in this paper were tested using real lung nodule data collected in the clinic. The test results demonstrate that the cascade classification method in this study achieves an accuracy of 80.04%, outperforming the conventional multi-classification approach. Conclusions: Different from the existing methods for categorizing the benign and malignant nature of lung nodules, the approach presented in this paper can classify lung nodules into 6 categories more accurately. At the same time, This paper proposes a rapid, precise, and dependable approach for classifying six distinct categories of lung nodules, which increases the accuracy categorization compared with the traditional multivariate categorization method.

Fast-Swimming Soft Robotic Fish Actuated by Bionic Muscle.

Soft underwater swimming robots actuated by smart materials have unique advantages in exploring the ocean, such as low noise, high flexibility, and friendly environment interaction ability. However, most of them typically exhibit limited swimming speed and flexibility due to the inherent characteristics of soft actuation materials. The actuation method and structural design of soft robots are key elements to improve their motion performance. Inspired by the muscle actuation and swimming mechanism of natural fish, a fast-swimming soft robotic fish actuated by a bionic muscle actuator made of dielectric elastomer is presented. The results show that by controlling the two independent actuating units of a biomimetic actuator, the robotic fish can not only achieve continuous C-shaped body motion similar to natural fish but also have a large bending angle (maximum unidirectional angle is about 40°) and thrust force (peak thrust is about 14 mN). In addition, the coupling relationship between the swimming speed and actuating parameters of the robotic fish is established through experiments and theoretical analysis. By optimizing the control strategy, the robotic fish can demonstrate a fast swimming speed of 76 mm/s (0.76 body length/s), which is much faster than most of the reported soft robotic fish driven by nonbiological soft materials that swim in body and/or caudal fin propulsion mode. What's more, by applying programmed voltage excitation to the actuating units of the bionic muscle, the robotic fish can be steered along specific trajectories, such as continuous turning motions and an S-shaped routine. This study is beneficial for promoting the design and development of high-performance soft underwater robots, and the adopted biomimetic mechanisms, as well as actuating methods, can be extended to other various flexible devices and soft robots.

An end-to-end multi-scale airway segmentation framework based on pulmonary CT image.

Objective. Automatic and accurate airway segmentation is necessary for lung disease diagnosis. The complex tree-like structures leads to gaps in the different generations of the airway tree, and thus airway segmentation is also considered to be a multi-scale problem. In recent years, convolutional neural networks have facilitated the development of medical image segmentation. In particular, 2D CNNs and 3D CNNs can extract different scale features. Hence, we propose a two-stage and 2D + 3D framework for multi-scale airway tree segmentation.Approach. In stage 1, we use a 2D full airway SegNet(2D FA-SegNet) to segment the complete airway tree. Multi-scale atros spatial pyramid and Atros Residual Skip connection modules are inserted to extract different scales feature. We designed a hard sample selection strategy to increase the proportion of intrapulmonary airway samples in stage 2. 3D airway RefineNet (3D ARNet) as stage 2 takes the results of stage 1 asa prioriinformation. Spatial information extracted by 3D convolutional kernel compensates for the loss of in 2D FA-SegNet. Furthermore, we added false positive losses and false negative losses to improve the segmentation performance of airway branches within the lungs.Main results. We performed data enhancement on the publicly available dataset of ISICDM 2020 Challenge 3, and on which evaluated our method. Comprehensive experiments show that the proposed method has the highest dice similarity coefficient (DSC) of 0.931, and IoU of 0.871 for the whole airway tree and DSC of 0.699, and IoU of 0.543 for the intrapulmonary bronchi tree. In addition, 3D ARNet proposed in this paper cascaded with other state-of-the-art methods to increase detected tree length rate by up to 46.33% and detected tree branch rate by up to 42.97%.Significance. The quantitative and qualitative evaluation results show that our proposed method performs well in segmenting the airway at different scales.

A bimodal feature fusion convolutional neural network for detecting obstructive sleep apnea/hypopnea from nasal airflow and oximetry signals.

The most prevalent sleep-disordered breathing condition is Obstructive Sleep Apnea (OSA), which has been linked to various health consequences, including cardiovascular disease (CVD) and even sudden death. Therefore, early detection of OSA can effectively help patients prevent the diseases induced by it. However, many existing methods have low accuracy in detecting hypopnea events or even ignore them altogether. According to the guidelines provided by the American Academy of Sleep Medicine (AASM), two modal signals, namely nasal pressure airflow and pulse oxygen saturation (SpO2), offer significant advantages in detecting OSA, particularly hypopnea events. Inspired by this notion, we propose a bimodal feature fusion CNN model that primarily comprises of a dual-branch CNN module and a feature fusion module for the classification of 10-second-long segments of nasal pressure airflow and SpO2. Additionally, an Efficient Channel Attention mechanism (ECA) is incorporated into the second module to adaptively weight feature map of each channel for improving classification accuracy. Furthermore, we design an OSA Severity Assessment Framework (OSAF) to aid physicians in effectively diagnosing OSA severity. The performance of both the bimodal feature fusion CNN model and OSAF is demonstrated to be excellent through per-segment and per-patient experimental results, based on the evaluation of our method using two real-world datasets consisting of polysomnography (PSG) recordings from 450 subjects.

Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes.

BACKGROUND: Accurate prediction of tumor molecular alterations is vital for optimizing cancer treatment. Traditional tissue-based approaches encounter limitations due to invasiveness, heterogeneity, and molecular dynamic changes. We aim to develop and validate a deep learning radiomics framework to obtain imaging features that reflect various molecular changes, aiding first-line treatment decisions for cancer patients. METHODS: We conducted a retrospective study involving 508 NSCLC patients from three institutions, incorporating CT images and clinicopathologic data. Two radiomic scores and a deep network feature were constructed on three data sources in the 3D tumor region. Using these features, we developed and validated the 'Deep-RadScore,' a deep learning radiomics model to predict prognostic factors, gene mutations, and immune molecule expression levels. FINDINGS: The Deep-RadScore exhibits strong discrimination for tumor molecular features. In the independent test cohort, it achieved impressive AUCs: 0.889 for lymphovascular invasion, 0.903 for pleural invasion, 0.894 for T staging; 0.884 for EGFR and ALK, 0.896 for KRAS and PIK3CA, 0.889 for TP53, 0.895 for ROS1; and 0.893 for PD-1/PD-L1. Fusing features yielded optimal predictive power, surpassing any single imaging feature. Correlation and interpretability analyses confirmed the effectiveness of customized deep network features in capturing additional imaging phenotypes beyond known radiomic features. INTERPRETATION: This proof-of-concept framework demonstrates that new biomarkers across imaging features and molecular phenotypes can be provided by fusing radiomic features and deep network features from multiple data sources. This holds the potential to offer valuable insights for radiological phenotyping in characterizing diverse tumor molecular alterations, thereby advancing the pursuit of non-invasive personalized treatment for NSCLC patients.

Nanomechanical Analysis of Living Small Extracellular Vesicles to Identify Gastric Cancer Cell Malignancy Based on a Biomimetic Peritoneum.

Gastric cancer is one of the most prevalent digestive malignancies. The lack of effective in vitro peritoneal models has hindered the exploration of the potential mechanisms behind gastric cancer's peritoneal metastasis. An accumulating body of research indicates that small extracellular vesicles (sEVs) play an indispensable role in peritoneal metastasis of gastric cancer cells. In this study, a biomimetic peritoneum was constructed. The biomimetic model is similar to real peritoneum in internal microstructure, composition, and primary function, and it enables the recurrence of peritoneal metastasis process in vitro. Based on this model, the association between the mechanical properties of sEVs and the invasiveness of gastric cancer was identified. By performing nanomechanical analysis on sEVs, we found that the Young's modulus of sEVs can be utilized to differentiate between malignant clinical samples (ascites) and nonmalignant clinical samples (peritoneal lavage). Furthermore, patients' ascites-derived sEVs were verified to stimulate the mesothelial-to-mesenchymal transition, thereby promoting peritoneal metastasis. In summary, nanomechanical analysis of living sEVs could be utilized for the noninvasive diagnosis of malignant degree and peritoneal metastasis of gastric cancer. This finding is expected to contribute future treatments.

Steering Muscle-Based Bio-Syncretic Robot Through Bionic Optimized Biped Mechanical Design.

Bio-syncretic robots consisting of artificial structures and living muscle cells have attracted much attention owing to their potential advantages, such as high drive efficiency, miniaturization, and compatibility. Motion controllability, as an important factor related to the main performance of bio-syncretic robots, has been explored in numerous studies. However, most of the existing bio-syncretic robots still face challenges related to the further development of steerable kinematic dexterity. In this study, a bionic optimized biped fully soft bio-syncretic robot actuated by two muscle tissues and steered with a direction-controllable electric field generated by external circularly distributed multiple electrodes has been developed. The developed bio-syncretic robot could realize wirelessly steerable motion and effective transportation of microparticle cargo on artificial polystyrene and biological pork tripe surfaces. This study may provide an effective strategy for the development of bio-syncretic robots and other related studies, such as nonliving soft robot design and muscle tissue engineering.