Research on system of ultra-flat carrying robot based on improved PSO algorithm

Ultra-flat carrying robots (UCR) are used to carry soft targets for functional safety road tests of intelligent driving vehicles and should have superior control performance. For the sake of analyzing and upgrading the motion control performance of the ultra-flat carrying robot, this paper develops the mathematical model of its motion control system on the basis of the test data and the system identification method. Aiming at ameliorating the defects of the standard particle swarm optimization (PSO) algorithm, namely, low accuracy, being susceptible to being caught in a local optimum, and slow convergence when dealing with the parameter identification problems of complex systems, this paper proposes a refined PSO algorithm with inertia weight cosine adjustment and introduction of natural selection principle (IWCNS-PSO), and verifies the superiority of the algorithm by test functions. Based on the IWCNS-PSO algorithm, the identification of transfer functions in the motion control system of the ultra-flat carrying robot was completed. In comparison with the identification results of the standard PSO and linear decreasing inertia weight (LDIW)-PSO algorithms, it indicated that the IWCNS-PSO has the optimal performance, with the number of iterations it takes to reach convergence being only 95 and the fitness value being only 0.117. The interactive simulation model was constructed in MATLAB/Simulink, and the critical proportioning method and the IWCNS-PSO algorithm were employed respectively to complete the tuning and optimization of the Proportional-Integral (PI) controller parameters. The results of simulation indicated that the PI parameters optimized by the IWCNS-PSO algorithm reduce the adjustment time to 7.99 s and the overshoot to 13.41% of the system, and the system is significantly improved with regard to the control performance, which basically meets the performance requirements of speed, stability, and accuracy for the control system. In conclusion, the IWCNS-PSO algorithm presented in this paper represents an efficient system identification method, as well as a system optimization method

Impact Response Comparison Between Parametric Human Models and Postmortem Human Subjects with a Wide Range of Obesity Levels

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138906/1/oby21947_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138906/2/oby21947.pd

Rapid Estimation of Binding Activity of Influenza Virus Hemagglutinin to Human and Avian Receptors

A critical step for avian influenza viruses to infect human hosts and cause epidemics or pandemics is acquisition of the ability of the viral hemagglutinin (HA) to bind to human receptors. However, current global influenza surveillance does not monitor HA binding specificity due to a lack of rapid and reliable assays. Here we report a computational method that uses an effective scoring function to quantify HA-receptor binding activities with high accuracy and speed. Application of this method reveals receptor specificity changes and its temporal relationship with antigenicity changes during the evolution of human H3N2 viruses. The method predicts that two amino acid differences at 222 and 225 between HAs of A/Fujian/411/02 and A/Panama/2007/99 viruses account for their differences in binding to both avian and human receptors; this prediction was verified experimentally. The new computational method could provide an urgently needed tool for rapid and large-scale analysis of HA receptor specificities for global influenza surveillance.National Key Project (2008ZX10004-013)National Institutes of Health (U.S.) (grant AI07443)Singapore-MIT Alliance for Research and TechnologyMassachusetts Institute of Technology. International Science and Technology Initiatives Global Seed FundNational Basic Research Program (973 Program) (2009CB918503)National Basic Research Program (973 Program) (2006CB911002

Ndrg2 regulates vertebral specification in differentiating somites

AbstractIt is generally thought that vertebral patterning and identity are globally determined prior to somite formation. Relatively little is known about the regulators of vertebral specification after somite segmentation. Here, we demonstrated that Ndrg2, a tumor suppressor gene, was dynamically expressed in the presomitic mesoderm (PSM) and at early stage of differentiating somites. Loss of Ndrg2 in mice resulted in vertebral homeotic transformations in thoracic/lumbar and lumbar/sacral transitional regions in a dose-dependent manner. Interestingly, the inactivation of Ndrg2 in osteoblasts or chondrocytes caused defects resembling those observed in Ndrg2−/− mice, with a lower penetrance. In addition, forced overexpression of Ndrg2 in osteoblasts or chondrocytes also conferred vertebral defects, which were distinct from those in Ndrg2−/− mice. These genetic analyses revealed that Ndrg2 modulates vertebral identity in segmented somites rather than in the PSM. At the molecular level, combinatory alterations of the amount of Hoxc8-11 gene transcripts were detected in the differentiating somites of Ndrg2−/− embryos, which may partially account for the vertebral defects in Ndrg2 mutants. Nevertheless, Bmp/Smad signaling activity was elevated in the differentiating somites of Ndrg2−/− embryos. Collectively, our findings unveiled Ndrg2 as a novel regulator of vertebral specification in differentiating somites

Prediction model of obstructive sleep apnea–related hypertension: Machine learning–based development and interpretation study

BackgroundObstructive sleep apnea (OSA) is a globally prevalent disease closely associated with hypertension. To date, no predictive model for OSA-related hypertension has been established. We aimed to use machine learning (ML) to construct a model to analyze risk factors and predict OSA-related hypertension.Materials and methodsWe retrospectively collected the clinical data of OSA patients diagnosed by polysomnography from October 2019 to December 2021 and randomly divided them into training and validation sets. A total of 1,493 OSA patients with 27 variables were included. Independent risk factors for the risk of OSA-related hypertension were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), bootstrapped aggregating (Bagging), and the multilayer perceptron (MLP), were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model. We compared the accuracy and discrimination of the models to identify the best machine learning algorithm for predicting OSA-related hypertension. In addition, a web-based tool was developed to promote its clinical application. We used permutation importance and Shapley additive explanations (SHAP) to determine the importance of the selected features and interpret the ML models.ResultsA total of 18 variables were selected for the models. The GBM model achieved the most extraordinary discriminatory ability (area under the receiver operating characteristic curve = 0.873, accuracy = 0.885, sensitivity = 0.713), and on the basis of this model, an online tool was built to help clinicians optimize OSA-related hypertension patient diagnosis. Finally, age, family history of hypertension, minimum arterial oxygen saturation, body mass index, and percentage of time of SaO2 < 90% were revealed by the SHAP method as the top five critical variables contributing to the diagnosis of OSA-related hypertension.ConclusionWe established a risk prediction model for OSA-related hypertension patients using the ML method and demonstrated that among the six ML models, the gradient boosting machine model performs best. This prediction model could help to identify high-risk OSA-related hypertension patients, provide early and individualized diagnoses and treatment plans, protect patients from the serious consequences of OSA-related hypertension, and minimize the burden on society

Label-free analysis of protein biomarkers using pattern-optimized graphene-nanopyramid SERS for rapid diagnosis of Alzheimer’s disease

The quantitative and highly sensitive detection of biomarkers such as Tau proteins and Aβ polypeptides is considered one of the most effective methods for the early diagnosis of Alzheimer’s disease (AD). Surface-enhanced Raman spectroscopy (SERS) detection is a promising method that faces, however, challenges like insufficient sensitivity due to the non-optimized nanostructures for specialized analyte sizes and insufficient control of the location of SERS hot spots. Thus, the SERS detection of AD biomarkers is restricted. We reported here an in-depth study of the analytical Raman enhancement factor (EF) of the wafer-scale graphene-Au nanopyramid hybrid SERS substrates using a combination of both theoretical calculation and experimental measurements. Experimental results show that larger nanopyramids and smaller gap spacing lead to a larger SERS EF, with an optimized analytical EF up to 1.1 × 1010. The hybrid SERS substrate exhibited detection limits of 10–15 M for Tau and phospho-Tau (P-Tau) proteins and 10–14 M for Aβ polypeptides, respectively. Principal component analysis correctly categorized the SERS spectra of different biomarkers at ultralow concentrations (10–13 M) using the optimized substrate. Amide III bands at 1200–1300 cm–1 reflect different structural conformations of proteins or polypeptides. Tau and P-Tau proteins are inherently disordered with a few α-helix residuals. The structure of Aβ42 polypeptides transitioned from the α-helix to the β-sheet as the concentration increased. These results demonstrate that the hybrid SERS method could be a simple and effective way for the label-free detection of protein biomarkers to enable the rapid early diagnosis of AD and other diseases

Prophylactic and therapeutic potential of magnolol-loaded PLGA-PEG nanoparticles in a chronic murine model of allergic asthma

Magnolol is a chemically defined and active polyphenol extracted from magnolia plants possessing anti-allergic activity, but its low solubility and rapid metabolism dramatically hinder its clinical application. To improve the therapeutic effects, magnolol-encapsulated polymeric poly (DL-lactide-co-glycolide)–poly (ethylene glycol) (PLGA-PEG) nanoparticles were constructed and characterized. The prophylactic and therapeutic efficacy in a chronic murine model of OVA-induced asthma and the mechanisms were investigated. The results showed that administration of magnolol-loaded PLGA-PEG nanoparticles significantly reduced airway hyperresponsiveness, lung tissue eosinophil infiltration, and levels of IL-4, IL-13, TGF-β1, IL-17A, and allergen-specific IgE and IgG1 in OVA-exposed mice compared to their empty nanoparticles-treated mouse counterparts. Magnolol-loaded PLGA-PEG nanoparticles also significantly prevented mouse chronic allergic airway mucus overproduction and collagen deposition. Moreover, magnolol-encapsulated PLGA-PEG nanoparticles showed better therapeutic effects on suppressing allergen-induced airway hyperactivity, airway eosinophilic inflammation, airway collagen deposition, and airway mucus hypersecretion, as compared with magnolol-encapsulated poly (lactic-co-glycolic acid) (PLGA) nanoparticles or magnolol alone. These data demonstrate the protective effect of magnolol-loaded PLGA-PEG nanoparticles against the development of allergic phenotypes, implicating its potential usefulness for the asthma treatment