Contact-Implicit Model Predictive Control for Dexterous In-hand Manipulation: A Long-Horizon and Robust Approach

Dexterous in-hand manipulation is an essential skill of production and life. Nevertheless, the highly stiff and mutable features of contacts cause limitations to real-time contact discovery and inference, which degrades the performance of model-based methods. Inspired by recent advancements in contact-rich locomotion and manipulation, this paper proposes a novel model-based approach to control dexterous in-hand manipulation and overcome the current limitations. The proposed approach has the attractive feature, which allows the robot to robustly execute long-horizon in-hand manipulation without pre-defined contact sequences or separated planning procedures. Specifically, we design a contact-implicit model predictive controller at high-level to generate real-time contact plans, which are executed by the low-level tracking controller. Compared with other model-based methods, such a long-horizon feature enables replanning and robust execution of contact-rich motions to achieve large-displacement in-hand tasks more efficiently; Compared with existing learning-based methods, the proposed approach achieves the dexterity and also generalizes to different objects without any pre-training. Detailed simulations and ablation studies demonstrate the efficiency and effectiveness of our method. It runs at 20Hz on the 23-degree-of-freedom long-horizon in-hand object rotation task.Comment: 7 pages, 8 figures, submitted to IROS202

Joint Learning of Deep Texture and High-Frequency Features for Computer-Generated Image Detection

Distinguishing between computer-generated (CG) and natural photographic (PG) images is of great importance to verify the authenticity and originality of digital images. However, the recent cutting-edge generation methods enable high qualities of synthesis in CG images, which makes this challenging task even trickier. To address this issue, a joint learning strategy with deep texture and high-frequency features for CG image detection is proposed. We first formulate and deeply analyze the different acquisition processes of CG and PG images. Based on the finding that multiple different modules in image acquisition will lead to different sensitivity inconsistencies to the convolutional neural network (CNN)-based rendering in images, we propose a deep texture rendering module for texture difference enhancement and discriminative texture representation. Specifically, the semantic segmentation map is generated to guide the affine transformation operation, which is used to recover the texture in different regions of the input image. Then, the combination of the original image and the high-frequency components of the original and rendered images are fed into a multi-branch neural network equipped with attention mechanisms, which refines intermediate features and facilitates trace exploration in spatial and channel dimensions respectively. Extensive experiments on two public datasets and a newly constructed dataset with more realistic and diverse images show that the proposed approach outperforms existing methods in the field by a clear margin. Besides, results also demonstrate the detection robustness and generalization ability of the proposed approach to postprocessing operations and generative adversarial network (GAN) generated images

PO-129 Effect of endurance training on liver NK cells in mice

Objective NK cell (natural killer cell) is a large granular lymphocyte distinct from a group of T and B lymphocytes. At present, the research shows that NK cells can specifically identify target cells and release killing media and then play a killing effect. It is confirmed that the expression of IL-15 is closely related to the differentiation and maturation of NK cells. Furthermore, skeletal muscle is an endocrine tissue and plays a key role in regulating the whole-body metabolic health by synthesizing and releasing humoral factors called myokines, such as IL-15. Whether the IL-15 induced by exercise training can promote the maturation of NK cells remain unsolved. This study aimed to explore the effects of moderate endurance training on NK cells and relative mechanism. Methods Twenty male C57BL/6J mice were randomly divided into 2 groups: control group (YC) and exercise group (YE). YC animals were fed normally for 12 weeks, YE animals were trained for 12 weeks on moderate intensity treadmill (12 m/min).Then the samples were isolated and RT-PCR was used to detect IL-15 and Nkg2d genes in the liver, Western blotting was used to detect the killer factor IFN-γ released by NK cells. Flow cytometry was used to detect NK1.1 cell markers in primary liver cells . Results 1)Compared with the YC group, the expression level of IL-15 and Nkg2d gene in the liver tissue of YE mice increased significantly (P < 0.05,P < 0.01); 2) Compared with the YC group, the expression of IFN-γ protein in the liver tissue of the YE mice increased significantly (P < 0.05); 3) Compared with two group. The proportion of NK cells in liver cells of group YE increased significantly (P < 0.05). Conclusions Moderate intensity endurance training can enhance the content and killing ability of NK cells through induced IL-15 in the liver

PL-019 Effect of early exercise on autophagy of liver tumor in mice: There is no full paper associated with this abstract

Objective To investigate whether the liver autophagy level can be altered by pre exercise training in mice liver tumors. Methods 40 Male C57BL/6J mice aged 7 months were randomly divided into 2 groups: control group (YC) and exercise group (YE). YE were exercised on a treadmill for 12 weeks (12m/min). After12 weeks each group was randomly divided into two groups. The tumor model was constructed by injection of HEPA1- 6 mouse hepatoma cell into liver tissue.Then the groups were control group (YC), exercise group (YE), tumor group (YCT), exercise tumor group (YET).The experimental samples were prepared on the 13 day after the tumor model was constructed. the hematoxylin and eosin stain of the liver was observed.The expression of autophagy related protein BECLIN1, LC3-II and ATG5 in liver tissues of mice was detected by Western blot. Results Compared with YCT group,the boundary of inflammatory cells and tumor cells in YET group was clear with normal cells.Compared with YCT group, the expression levels of BECLIN1, LC3-II and ATG5 in liver tissue of YET group were significantly higher (p < 0.01, P < 0.01, P < 0.05). Conclusions Early exercise can help the 7 month old mice to resist the occurrence and development of the liver tumor. It's probably associated with increased level of autophagy in the liver by early exercise training

A non-enhanced CT-based deep learning diagnostic system for COVID-19 infection at high risk among lung cancer patients

BackgroundPneumonia and lung cancer have a mutually reinforcing relationship. Lung cancer patients are prone to contracting COVID-19, with poorer prognoses. Additionally, COVID-19 infection can impact anticancer treatments for lung cancer patients. Developing an early diagnostic system for COVID-19 pneumonia can help improve the prognosis of lung cancer patients with COVID-19 infection.MethodThis study proposes a neural network for COVID-19 diagnosis based on non-enhanced CT scans, consisting of two 3D convolutional neural networks (CNN) connected in series to form two diagnostic modules. The first diagnostic module classifies COVID-19 pneumonia patients from other pneumonia patients, while the second diagnostic module distinguishes severe COVID-19 patients from ordinary COVID-19 patients. We also analyzed the correlation between the deep learning features of the two diagnostic modules and various laboratory parameters, including KL-6.ResultThe first diagnostic module achieved an accuracy of 0.9669 on the training set and 0.8884 on the test set, while the second diagnostic module achieved an accuracy of 0.9722 on the training set and 0.9184 on the test set. Strong correlation was observed between the deep learning parameters of the second diagnostic module and KL-6.ConclusionOur neural network can differentiate between COVID-19 pneumonia and other pneumonias on CT images, while also distinguishing between ordinary COVID-19 patients and those with white lung. Patients with white lung in COVID-19 have greater alveolar damage compared to ordinary COVID-19 patients, and our deep learning features can serve as an imaging biomarker

Fibroblast Growth Factor–21 ameliorates hepatic encephalopathy by activating the STAT3-SOCS3 pathway to inhibit activated hepatic stellate cells

Neurological dysfunction, one of the consequences of acute liver failure (ALF), and also referred to as hepatic encephalopathy (HE), contributes to mortality posing challenges for clinical management. FGF21 has been implicated in the inhibition of cognitive decline and fibrogenesis. However, the effects of FGF21 on the clinical and molecular presentations of HE has not been elucidated. HE was induced by fulminant hepatic failure using thioacetamide (TAA) in male C57BL/6J mice while controls were injected with saline. For two consecutive weeks, mice were treated intraperitoneally with FGF21 (3 mg/kg) while controls were treated with saline. Cognitive, neurological, and activity function scores were recorded. Serum, liver, and brain samples were taken for analysis of CCL5 and GABA by ELISA, and RT qPCR was used to measure the expressions of fibrotic and pro-inflammatory markers. We report significant improvement in both cognitive and neurological scores by FGF21 treatment after impairment by TAA. GABA and CCL5, key factors in the progression of HE were also significantly reduced in the treatment group. Furthermore, the expression of fibrotic markers such as TGFβ and Col1 were also significantly downregulated after FGF21 treatment. TNFα and IL-6 were significantly reduced in the liver while in the brain, TNFα and IL-1 were downregulated. However, both in the liver and the brain, IL-10 was significantly upregulated. FGF21 inhibits CXCR4/CCL5 activation and upregulates the production of IL-10 in the damaged liver stimulating the production pro-inflammatory cytokines and apoptosis of hepatic stellate cells through the STAT3-SOCS3 pathway terminating the underlying fibrosis in HE

Twelve-week treadmill endurance training in mice is associated with upregulation of interleukin-15 and natural killer cell activation and increases apoptosis rate in Hepa1-6 cell-derived mouse hepatomas

Regular exercise reduces the risk of malignancy and decreases the recurrence of cancer. However, the mechanisms behind this protection remain to be elucidated. Natural killer (NK) cells are lymphocytes of the innate immune system, which play essential roles in immune defense and effectively prevent cancer metastasis. Physical exercise can increase the activity of NK cells. Interleukin-15 (IL-15) is the best-studied cytokine activator of NK cells, and it was shown to have many positive functional effects on NK cells to improve antitumor responses. The aim of this study was to clarify the possible important mechanisms behind endurance exercise-induced changes in NK cell function, which may be highly correlated with IL-15. An animal model was used to study IL-15 expression level, tumor volume, cancer cell apoptosis, and NK cell infiltration after treadmill exercise. Although IL-15 was highly expressed in skeletal muscle, treadmill exercise further elevated IL-15 levels in plasma and muscle (P<0.05). In addition, tumor weight and volume of tumor-bearing mice were decreased (P<0.05), and liver tumor cell apoptosis was increased after 12 weeks of treadmill exercise (P<0.05). NK cell infiltration was upregulated in tumors from treadmill exercise mice, and the level of interferon-gamma (IFN-γ) and IL-15 were higher than in sedentary mice (P<0.05). The study indicated that regular endurance training can reduce cancer risk, which was related to increased IL-15 expression, activation of the immune killing effect of NK cells, and promotion of tumor cell apoptosis, which can ultimately control tumor growth

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io