A Consensus Algorithm Based on Risk Assessment Model for Permissioned Blockchain

Blockchain technology enables stakeholders to conduct trusted data sharing and exchange without a trusted centralized institution. These features make blockchain applications attractive to enhance trustworthiness in very different contexts. Due to unique design concepts and outstanding performance, blockchain has become a popular research topic in industry and academia in recent years. Every participant is anonymous in a permissionless blockchain represented by cryptocurrency applications such as Bitcoin. In this situation, some special incentive mechanisms are applied to permissionless blockchain, such as mined native cryptocurrency to solve the trust issues of permissionless blockchain. In many use cases, permissionless blockchain has bottlenecks in transaction throughput performance, which restricts further application in the real world. A permissioned blockchain can reach a consensus among a group of entities that do not establish an entire trust relationship. Unlike permissionless blockchains, the participants must be identified in permissioned blockchains. By relying on the traditional crash fault-tolerant consensus protocols, permissioned blockchains can achieve high transaction throughput and low latency without sacrificing security. However, how to balance the security and consensus efficiency is still the issue that needs to be solved urgently in permissioned blockchains. As the core module of blockchain technology, the consensus algorithm plays a vital role in the performance of the blockchain system. Thus, this paper proposes a new consensus algorithm for permissioned blockchain, the Risk Assessment-based Consensus protocol (RAC), combined with the decentralized design concept and the risk-node assessment mechanism to address the unbalance issues of performance in speed, scalability, and security.Comment: 32 pages, 11 figure

Spatiotemporal variations in vegetation cover on the Loess Plateau, China, between 1982 and 2013: possible causes and potential impacts

Vegetation is a key component of the ecosystem and plays an important role in water retention and resistance to soil erosion. In this study, we used a multiyear normalized difference vegetation index (NDVI) dataset (1982-2013) and corresponding datasets for observed climatic variables to analyze changes in the NDVI at both temporal and spatial scales. The relationships between NDVI, climate change, and human activities were also investigated. The annual average NDVI showed an upward trend over the 32-year study period, especially in the center of the Loess Plateau. NDVI variations lagged behind monthly temperature changes by approximately 1 month. The contribution of human activities to variations in NDVI has become increasingly significant in recent years, with human activities responsible for 30.4% of the change in NDVI during the period 2001-2013. The increased vegetation coverage has reduced soil erosion on the Loess Plateau in recent years. It is suggested that natural restoration of vegetation is the most effective measure for control of erosion; engineering measures that promote this should feature in the future governance of the Loess Plateau

A Bioinspired Bidirectional Stiffening Soft Actuator for Multimodal, Compliant, and Robust Grasping

The stiffness modulation mechanism for soft robotics has gained considerable attention to improve deformability, controllability, and stability. However, for the existing stiffness soft actuator, high lateral stiffness and a wide range of bending stiffness are hard to be provided at the same time. This paper presents a bioinspired bidirectional stiffening soft actuator (BISA) combining the air-tendon hybrid actuation (ATA) and a bone-like structure (BLS). The ATA is the main actuation of the BISA, and the bending stiffness can be modulated with a maximum stiffness of about 0.7 N/mm and a maximum magnification of 3 times when the bending angle is 45 deg. Inspired by the morphological structure of the phalanx, the lateral stiffness can be modulated by changing the pulling force of the BLS. The lateral stiffness can be modulated by changing the pulling force to it. The actuator with BLSs can improve the lateral stiffness about 3.9 times compared to the one without BLSs. The maximum lateral stiffness can reach 0.46 N/mm. And the lateral stiffness can be modulated decoupling about 1.3 times (e.g., from 0.35 N/mm to 0.46 when the bending angle is 45 deg). The test results show the influence of the rigid structures on bending is small with about 1.5 mm maximum position errors of the distal point of actuator bending in different pulling forces. The advantages brought by the proposed method enable a soft four-finger gripper to operate in three modes: normal grasping, inverse grasping, and horizontal lifting. The performance of this gripper is further characterized and versatile grasping on various objects is conducted, proving the robust performance and potential application of the proposed design method

Isolation and Characteristics of a Bacterial Strain for Deodorization of Dimethyl Sulfide

AbstractThe removal characteristics of dimethyl sulfide (DMS) with a peat packed tower were studied. The peat itself did not remove DMS. The peat inoculated with activated sludge as a source of microorganisms showed an efficient removal of DMS. Dominant microorganisms for degradation of DMS in the peat packed tower were some chemolithotrophic and non-acidophilic sulfur-oxidizing microorganisms originating from sludge. A dominant DMS-oxidizing strain Au7 was isolated and identified as chemolithotrophic Thiobacilli. Product of DMS oxidation by strain Au7 was sulfate. The optimum pH of DMS removal by strain Au7 was 7-5.45

Motion Mappings for Continuous Bilateral Teleoperation

Mapping operator motions to a robot is a key problem in teleoperation. Due to differences between workspaces, such as object locations, it is particularly challenging to derive smooth motion mappings that fulfill different goals (e.g. picking objects with different poses on the two sides or passing through key points). Indeed, most state-of-the-art methods rely on mode switches, leading to a discontinuous, low-transparency experience. In this paper, we propose a unified formulation for position, orientation and velocity mappings based on the poses of objects of interest in the operator and robot workspaces. We apply it in the context of bilateral teleoperation. Two possible implementations to achieve the proposed mappings are studied: an iterative approach based on locally-weighted translations and rotations, and a neural network approach. Evaluations are conducted both in simulation and using two torque-controlled Franka Emika Panda robots. Our results show that, despite longer training times, the neural network approach provides faster mapping evaluations and lower interaction forces for the operator, which are crucial for continuous, real-time teleoperation.Comment: Accepted for publication at the IEEE Robotics and Automation Letters (RA-L

Automated sleep state classification of wide-field calcium imaging data via multiplex visibility graphs and deep learning

BACKGROUND: Wide-field calcium imaging (WFCI) allows for monitoring of cortex-wide neural dynamics in mice. When applied to the study of sleep, WFCI data are manually scored into the sleep states of wakefulness, non-REM (NREM) and REM by use of adjunct EEG and EMG recordings. However, this process is time-consuming and often suffers from low inter- and intra-rater reliability and invasiveness. Therefore, an automated sleep state classification method that operates on WFCI data alone is needed. NEW METHOD: A hybrid, two-step method is proposed. In the first step, spatial-temporal WFCI data is mapped to multiplex visibility graphs (MVGs). Subsequently, a two-dimensional convolutional neural network (2D CNN) is employed on the MVGs to be classified as wakefulness, NREM and REM. RESULTS: Sleep states were classified with an accuracy of 84% and Cohen\u27s κ of 0.67. The method was also effectively applied on a binary classification of wakefulness/sleep (accuracy=0.82, κ = 0.62) and a four-class wakefulness/sleep/anesthesia/movement classification (accuracy=0.74, κ = 0.66). Gradient-weighted class activation maps revealed that the CNN focused on short- and long-term temporal connections of MVGs in a sleep state-specific manner. Sleep state classification performance when using individual brain regions was highest for the posterior area of the cortex and when cortex-wide activity was considered. COMPARISON WITH EXISTING METHOD: On a 3-hour WFCI recording, the MVG-CNN achieved a κ of 0.65, comparable to a κ of 0.60 corresponding to the human EEG/EMG-based scoring. CONCLUSIONS: The hybrid MVG-CNN method accurately classifies sleep states from WFCI data and will enable future sleep-focused studies with WFCI

PCBP-1 regulates alternative splicing of the CD44 gene and inhibits invasion in human hepatoma cell line HepG2 cells

<p>Abstract</p> <p>Background</p> <p>PCBP1 (or alpha CP1 or hnRNP E1), a member of the PCBP family, is widely expressed in many human tissues and involved in regulation of transcription, transportation process, and function of RNA molecules. However, the role of PCBP1 in CD44 variants splicing still remains elusive.</p> <p>Results</p> <p>We found that enforced PCBP1 expression inhibited CD44 variants expression including v3, v5, v6, v8, and v10 in HepG2 cells, and knockdown of endogenous PCBP1 induced these variants splicing. Invasion assay suggested that PCBP1 played a negative role in tumor invasion and re-expression of v6 partly reversed the inhibition effect by PCBP1. A correlation of PCBP1 down-regulation and v6 up-regulation was detected in primary HCC tissues.</p> <p>Conclusions</p> <p>We first characterized PCBP1 as a negative regulator of CD44 variants splicing in HepG2 cells, and loss of PCBP1 in human hepatic tumor contributes to the formation of a metastatic phenotype.</p

Targeting immunosuppressive Ly6C+ classical monocytes reverses anti-PD-1/CTLA-4 immunotherapy resistance

IntroductionDespite significant clinical advancement with the use of immune checkpoint blockade (ICB) in non-small cell lung cancer (NSCLC) there are still a major subset of patients that develop adaptive/acquired resistance. Understanding resistance mechanisms to ICB is critical to developing new therapeutic strategies and improving patient survival. The dynamic nature of the tumor microenvironment and the mutational load driving tumor immunogenicity limit the efficacy to ICB. Recent studies indicate that myeloid cells are drivers of ICB resistance. In this study we sought to understand which immune cells were contributing to resistance and if we could modify them in a way to improve response to ICB therapy.ResultsOur results show that combination anti-PD-1/CTLA-4 produces an initial antitumor effect with evidence of an activated immune response. Upon extended treatment with anti-PD-1/CTLA-4 acquired resistance developed with an increase of the immunosuppressive populations, including T-regulatory cells, neutrophils and monocytes. Addition of anti-Ly6C blocking antibody to anti-PD-1/CTLA-4 was capable of completely reversing treatment resistance and restoring CD8 T cell activity in multiple KP lung cancer models and in the autochthonous lung cancer KrasLSL-G12D/p53fl/fl model. We found that there were higher classical Ly6C+ monocytes in anti-PD-1/CTLA-4 combination resistant tumors. B7 blockade illustrated the importance of dendritic cells for treatment efficacy of anti-Ly6C/PD-1/CTLA-4. We further determined that classical Ly6C+ monocytes in anti-PD-1/CTLA-4 resistant tumors are trafficked into the tumor via IFN-γ and the CCL2-CCR2 axis. Mechanistically we found that classical monocytes from ICB resistant tumors were unable to differentiate into antigen presenting cells and instead differentiated into immunosuppressive M2 macrophages or myeloid-derived suppressor cells (MDSC). Classical Ly6C+ monocytes from ICB resistant tumors had a decrease in both Flt3 and PU.1 expression that prevented differentiation into dendritic cells/macrophages.ConclusionsTherapeutically we found that addition of anti-Ly6C to the combination of anti-PD-1/CTLA-4 was capable of complete tumor eradication. Classical Ly6C+ monocytes differentiate into immunosuppressive cells, while blockade of classical monocytes drives dendritic cell differentiation/maturation to reinvigorate the anti-tumor T cell response. These findings support that immunotherapy resistance is associated with infiltrating monocytes and that controlling the differentiation process of monocytes can enhance the therapeutic potential of ICB

Extended Averaged Learning Subspace Method for Hyperspectral Data Classification

Averaged learning subspace methods (ALSM) have the advantage of being easily implemented and appear to outperform in classification problems of hyperspectral images. However, there remain some open and challenging problems, which if addressed, could further improve their performance in terms of classification accuracy. We carried out experiments mainly by using two kinds of improved subspace methods (namely, dynamic and fixed subspace methods), in conjunction with the [0,1] and [-1,+1] normalization methods. We used different performance indicators to support our experimental studies: classification accuracy, computation time, and the stability of the parameter settings. Results are presented for the AVIRIS Indian Pines data set. Experimental analysis showed that the fixed subspace method combined with the [0,1] normalization method yielded higher classification accuracy than other subspace methods. Moreover, ALSMs are easily applied: only two parameters need to be set, and they can be applied directly to hyperspectral data. In addition, they can completely identify training samples in a finite number of iterations

Over-Expression of LSD1 Promotes Proliferation, Migration and Invasion in Non-Small Cell Lung Cancer

Background: Lysine specific demethylase 1 (LSD1) has been identified and biochemically characterized in epigenetics, but the pathological roles of its dysfunction in lung cancer remain to be elucidated. The aim of this study was to evaluate the prognostic significance of LSD1 expression in patients with non-small cell lung cancer (NSCLC) and to define its exact role in lung cancer proliferation, migration and invasion. Methods: The protein levels of LSD1 in surgically resected samples from NSCLC patients were detected by immunohistochemistry or Western blotting. The mRNA levels of LSD1 were detected by qRT-PCR. The correlation of LSD1 expression with clinical characteristics and prognosis was determined by statistical analysis. Cell proliferation rate was assessed by MTS assay and immunofluorescence. Cell migration and invasion were detected by scratch test, matrigel assay and transwell invasion assay. Results: LSD1 expression was higher in lung cancer tissue more than in normal lung tissue. Our results showed that overexpression of LSD1 protein were associated with shorter overall survival of NSCLC patients. LSD1 was localized mainly to the cancer cell nucleus. Interruption of LSD1 using siRNA or a chemical inhibitor, pargyline, suppressed proliferation, migration and invasion of A549, H460 and 293T cells. Meanwhile, over-expression of LSD1 enhanced cell growth. Finally, LSD1 was shown to regulate epithelial-to-mesenchymal transition in lung cancer cells