A novel algorithm of posture best fit based on key characteristics for large components assembly

Measurement and variation control of geometrical Key Characteristics (KCs), such as flatness and gap of joint faces, coaxiality of cabin sections, is the crucial issue in large components assembly from the aerospace industry. Aiming to control geometrical KCs and to attain the best fit of posture, an optimization algorithm based on KCs for large components assembly is proposed. This approach regards the posture best fit, which is a key activity in Measurement Aided Assembly (MAA), as a two-phase optimal problem. In the first phase, the global measurement coordinate system of digital model and shop floor is unified with minimum error based on singular value decomposition, and the current posture of components being assembly is optimally solved in terms of minimum variation of all reference points. In the second phase, the best posture of the movable component is optimally determined by minimizing multiple KCs' variation with the constraints that every KC respectively conforms to its product specification. The optimal models and the process procedures for these two-phase optimal problems based on Particle Swarm Optimization (PSO) are proposed. In each model, every posture to be calculated is modeled as a 6 dimensional particle (three movement and three rotation parameters). Finally, an example that two cabin sections of satellite mainframe structure are being assembled is selected to verify the effectiveness of the proposed approach, models and algorithms. The experiment result shows the approach is promising and will provide a foundation for further study and application. © 2013 The Authors

Single-molecule photon-fueled DNA nanoscissors for DNA cleavage based on the regulation of substrate binding affinity by azobenzene

National Basic Research Program of China [2010CB732402]; National Scientific Foundation of China [21205100, 21075104]; Natural Science Foundation of Fujian Province for Distinguished Young Scholars [2010 J06004]A pair of single-molecule photo-responsive DNA nanoscissors for DNA cleavage based on the regulation of substrate binding affinity was designed and fabricated. Compared with other DNA nanomachines, our DNA nanoscissors have the advantages of a clean switching mechanism, as well as robust and highly reversible operation

Apigenin Combined With Gefitinib Blocks Autophagy Flux and Induces Apoptotic Cell Death Through Inhibition of HIF-1α, c-Myc, p-EGFR, and Glucose Metabolism in EGFR L858R+T790M-Mutated H1975 Cells

Cancer cells are characterized by abnormally increased glucose uptake and active bio-energy and biosynthesis to support the proliferation, metastasis, and drug resistant survival. We examined the therapeutic value of the combination of apigenin (a natural small-molecule inhibitor of Glut1 belonging to the flavonoid family) and gefitinib on epidermal growth factor receptor (EGFR)-resistant mutant non-small cell lung cancer, to notably damage glucose utilization and thus suppress cell growth and malignant behavior. Here, we demonstrate that apigenin combined with gefitinib inhibits multiple oncogenic drivers such as c-Myc, HIF-1α, and EGFR, reduces Gluts and MCT1 protein expression, and inactivates the 5′ adenosine monophosphate-activated protein kinase (AMPK) signaling, which regulates glucose uptake and maintains energy metabolism, leading to impaired energy utilization in EGFR L858R-T790M-mutated H1975 lung cancer cells. H1975 cells exhibit dysregulated metabolism and apoptotic cell death following treatment with apigenin + gefitinib. Therefore, the combined apigenin + gefitinib treatment presents an attractive strategy as alternative treatment for the acquired resistance to EGFR-TKIs in NSCLC

Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification

Failure to recognize samples from the classes unseen during training is a major limit of artificial intelligence (AI) in real-world implementation of retinal anomaly classification. To resolve this obstacle, we propose an uncertainty-inspired open-set (UIOS) model which was trained with fundus images of 9 common retinal conditions. Besides the probability of each category, UIOS also calculates an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieved an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external testing set and non-typical testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicted high uncertainty scores, which prompted the need for a manual check, in the datasets of rare retinal diseases, low-quality fundus images, and non-fundus images. This work provides a robust method for real-world screening of retinal anomalies

A Novel Measurement Method for Linear Thermal Expansion Coefficient of Laminated Composite Material Tubular Specimen

Materials of satellite integration truss frame are required to withstand temperature that range from about – 250 °C ~ + 150 °C. In order to reduce structural components deformation caused by such temperature change, material of truss frame mostly adopts laminated composite material tubes, whose linear thermal expansion coefficient (LTEC) is very small. Therefore, accurate measurement of LTEC of truss frame materials over a broad temperature range is essential for successful mission. To address this issue, this paper proposes a general experiment platform for measuring LTEC of laminated composite material specimen reaching length up to one meter in the temperature range from – 100 °C to +100 °C. The platform uses light-density optical fiber probe to measure length variation and thermocouple to record temperature variation. Thereafter, the thermal expansion coefficient and its measurement uncertainty can be obtained by establishing and solving mathematical model. Finally, LTEC measurement of a tubular composite materials specimen is conducted. The experiment result demonstrates the validity and practicality of the experiment platform and the measurement accuracy of LTEC which can reach up to 10-7/°C.DOI: http://dx.doi.org/10.5755/j01.ms.21.4.9708</p

Effects of predictive nursing intervention on cognitive impairment and neurological function in ischemic stroke patients

Abstract Background Ischemic stroke is a clinical emergency caused by insufficient intracranial blood supply, which eventually leads to brain tissue necrosis and neurological impairment. Predictive nursing intervention has achieved impressive success in the nursing of multiple surgeries. However, the role of predictive nursing intervention in the care of patients with ischemic stroke remains unclear. Methods This study was a randomized controlled trial. Based on the inclusion and exclusion criteria, 126 patients were randomly assigned into two groups, namely the control group and the predictive nursing intervention group. Both groups were treated with thrombolytic therapy with alteplase. The patients in the control group were given routine nursing intervention and the predictive nursing intervention group received additional predictive care. Neurologic functions and cognitive impairment were evaluated by National Institutes of Health Stroke Scale (NIHSS), Fugl‐Meyer assessment (FMA), Montreal cognitive assessment (MoCA), and mini‐mental state examination (MMSE) scales, respectively. Door‐to‐Needle Times, venous thromboembolism (VTE)‐related parameters, and complications were recorded. Results Predictive nursing intervention significantly shortened the Door‐to‐Needle Times and enhanced the peak/average femoral venous blood flow and femoral venous diameter. In addition, predictive nursing intervention improved the NIHSS, FMA, MMSE, and MoCA scores and remarkably reduced the recurrence of ischemic stroke, deep vein thrombosis and gingival bleeding. Conclusion Predictive nursing intervention is beneficial to improve the effects of thrombolytic therapy in patients with ischemic stroke, which improves the neurological, cognitive and motor functions of patients, and reduces the occurrence of complications, suggesting an important clinical application value

Highly lethal genotype I and II recombinant African swine fever viruses detected in pigs

Abstract African swine fever virus (ASFV) poses a great threat to the global pig industry and food security. Currently, 24 ASFV genotypes have been reported but it is unclear whether recombination of different genotype viruses occurs in nature. In this study, we detect three recombinants of genotype I and II ASFVs in pigs in China. These recombinants are genetically similar and classified as genotype I according to their B646L gene, yet 10 discrete fragments accounting for over 56% of their genomes are derived from genotype II virus. Animal studies with one of the recombinant viruses indicate high lethality and transmissibility in pigs, and deletion of the virulence-related genes MGF_505/360 and EP402R derived from virulent genotype II virus highly attenuates its virulence. The live attenuated vaccine derived from genotype II ASFV is not protective against challenge of the recombinant virus. These naturally occurring recombinants of genotype I and II ASFVs have the potential to pose a challenge to the global pig industry