Quantum algebra of multiparameter Manin matrices

Multiparametric quantum semigroups $\mathrm{M}_{\hat{q}, \hat{p}}(n)$ are generalization of the one-parameter general linear semigroups $\mathrm{M}_q(n)$, where $\hat{q}=(q_{ij})$ and $\hat{p}=(p_{ij})$ are $2n^2$ parameters satisfying certain conditions. In this paper, we study the algebra of multiparametric Manin matrices using the R-matrix method. The systematic approach enables us to obtain several classical identities such as Muir identities, Newton's identities, Capelli-type identities, Cauchy-Binet's identity both for determinant and permanent as well as a rigorous proof of the MacMahon master equation for the quantum algebra of multiparametric Manin matrices. Some of the generalized identities are also generalized to multiparameter $q$-Yangians.Comment: 31 pages; final versio

Selective Determination of Pyridine Alkaloids in Tobacco by PFTBA Ions/Analyte Molecule Reaction Ionization Ion Trap Mass Spectrometry

The application of perfluorotributylamine (PFTBA) ions/analyte molecule reaction ionization for the selective determination of tobacco pyridine alkaloids by ion trap mass spectrometry (IT-MS) is reported. The main three PFTBA ions (CF3+, C3F5+, and C5F10N+) are generated in the external source and then introduced into ion trap for reaction with analytes. Because the existence of the tertiary nitrogen atom in the pyridine makes it possible for PFTBA ions to react smoothly with pyridine and forms adduct ions, pyridine alkaloids in tobacco were selectively ionized and formed quasi-molecular ion [M + H]+and adduct ions, including [M + 69]+, [M + 131]+, and [M + 264]+, in IT-MS. These ions had distinct abundances and were regarded as the diagnostic ions of each tobacco pyridine alkaloid for quantitative analysis in selected-ion monitoring mode. Results show that the limit of detection is 0.2 μg/mL, and the relative standard deviations for the seven alkaloids are in the range of 0.71% to 6.8%, and good recovery of 95.6% and 97.2%. The proposed method provides substantially greater selectivity and sensitivity compared with the conventional approach and offers an alternative approach for analysis of tobacco alkaloids

Monitoring Enzyme Reaction and Screening of Inhibitors of Acetylcholinesterase by Quantitative Matrix-Assisted Laser Desorption/Ionization Fourier Transform Mass Spectrometry

A matrix-assisted laser desorption/ionization Fourier transform mass spectrometry (MALDI-FTMS)–based assay was developed for kinetic measurements and inhibitor screening of acetylcholinesterase. Here, FTMS coupled to MALDI was applied to quantitative analysis of choline using the ratio of choline/acetylcholine without the use of additional internal standard, which simplified the experiment. The Michaelis constant (Km) of acetylcholinesterase (AChE) was determined to be 73.9 μmol L−1 by this approach. For Huperzine A, the linear mixed inhibition of AChE reflected the presence of competitive and noncompetitive components. The half maximal inhibitory concentration (IC50) value of galantamine obtained for AChE was 2.39 μmol L−1. Inhibitory potentials of Rhizoma Coptidis extracts were identified with the present method. In light of the results the referred extracts as a whole showed inhibitory action against AChE. The use of high-resolution FTMS largely eliminated the interference with the determination of ACh and Ch, produced by the low-mass compounds of chemical libraries for inhibitor screening. The excellent correlation with the reported kinetic parameters confirms that the MS-based assay is both accurate and precise for determining kinetic constants and for identifying enzyme inhibitors. The obvious advantages were demonstrated for quantitative analysis and also high-throughput characterization. This study offers a perspective into the utility of MALDI-FTMS as an alternate quantitative tool for inhibitor screening of AChE

Transformation Decoupling Strategy based on Screw Theory for Deterministic Point Cloud Registration with Gravity Prior

Point cloud registration is challenging in the presence of heavy outlier correspondences. This paper focuses on addressing the robust correspondence-based registration problem with gravity prior that often arises in practice. The gravity directions are typically obtained by inertial measurement units (IMUs) and can reduce the degree of freedom (DOF) of rotation from 3 to 1. We propose a novel transformation decoupling strategy by leveraging screw theory. This strategy decomposes the original 4-DOF problem into three sub-problems with 1-DOF, 2-DOF, and 1-DOF, respectively, thereby enhancing the computation efficiency. Specifically, the first 1-DOF represents the translation along the rotation axis and we propose an interval stabbing-based method to solve it. The second 2-DOF represents the pole which is an auxiliary variable in screw theory and we utilize a branch-and-bound method to solve it. The last 1-DOF represents the rotation angle and we propose a global voting method for its estimation. The proposed method sequentially solves three consensus maximization sub-problems, leading to efficient and deterministic registration. In particular, it can even handle the correspondence-free registration problem due to its significant robustness. Extensive experiments on both synthetic and real-world datasets demonstrate that our method is more efficient and robust than state-of-the-art methods, even when dealing with outlier rates exceeding 99%

Impacts of Future Climate Change on Net Primary Productivity of Grassland in Inner Mongolia, China

Net Primary Productivity (NPP) of grassland is a key variable of terrestrial ecosystems and is an important parameter for characterizing carbon cycles in grassland ecosystems. In this research, the Inner Mongolia grassland NPP was calculated using the Miami Model and the impact of climate change on grassland NPP was subsequently analyzed under the Special Report on Emissions Scenarios (SRES) A2, B2, and A1B scenarios, which are inferred from Providing Regional Climates for Impacts Studies (PRECIS) climate model system. The results showed that: (1) the NPP associated with these three scenarios had a similar distribution in Inner Mongolia: the grassland NPP increased gradually from the western region, with less than 200 g/m2/yr, to the southeast region, with more than 800 g/m2/yr. Precipitation was the main factor determining the grassland NPP; (2) compared with the baseline (1961-1990), there would be an overall increase in grassland NPP during three time periods (2020s: 2011-2040, 2050s: 2041-2070, and 2080s: 2071-2100) under the A2 and B2 scenarios; (3) under the A1B scenario, there will be a decreasing trend at middle-west region during the 2020s and 2050s; while there will be a very significant decrease from the 2050s to 2080s for middle Inner Mongolia; and (4) grassland NPP under the A1B scenario would present the most significant increase among the three scenarios, and would have the least significant increase under the B2 scenario

Favoritism Toward Foreign and Domestic Brands: A Comparison of Different Theoretical Explanations

Five theoretical approaches can predict favoritism toward domestic and foreign brands. This article applies a contrastive perspective to examine social identity, personal identity, cultural identity, system justification, and categorical cognition theories and their attendant constructs. The authors propose a set of main-effects hypotheses as well as hypotheses related to both product and country moderation effects on attitudes toward and loyalty to domestic and foreign brands. They test the hypotheses on a sample of Chinese consumers with respect to salient brands from 12 product categories. The results indicate that three of the theoretical approaches examined can explain only one side of favoritism—most commonly favoritism toward domestic brands—but not favoritism toward both domestic and foreign brands. Consumer xenocentrism, a concept rooted in system justification theory, seems to provide more consistent predictions for both domestic- and foreign-brand bias

Learning task-oriented dexterous grasping from human knowledge

Industrial automation requires robot dexterity to automate many processes such as product assembling, packaging, and material handling. The existing robotic systems lack the capability to determining proper grasp strategies in the context of object affordances and task designations. In this paper, a framework of task-oriented dexterous grasping is proposed to learn grasp knowledge from human experience and to deploy the grasp strategies while adapting to grasp context. Grasp topology is defined and grasp strategies are learned from an established dataset for task-oriented dexterous manipulation. To adapt to various grasp context, a reinforcement-learning based grasping policy was implement to deploy different task-oriented strategies. The performances of the system was evaluated in a simulated grasping environment by using an AR10 anthropomorphic hand installed in a Sawyer robotic arm. The proposed framework achieved a hit rate of 100% for grasp strategies and an overall top-3 match rate of 95.6%. The success rate of grasping was 85.6% during 2700 grasping experiments for manipulation tasks given in natural-language instructions

A Multi-scale Approach to Investigating the Wintering Habitat Selection of Red-crowned Cranes in the Yancheng Nature Reserve, China

A B S T R A C T The red-crowned crane (Grus japonensis) is a rare and endangered species that lives in wetland habitats. In this study, we first compared crane habitat selection in December, 2013 and January, 2014 using the Neu method in the Yancheng National Reserve (YNR). We then explored the relative importance of habitats (plot, landscape) and spatial factors on red-crowned crane abundance at multiple scales using regression models and variation partitioning approaches. Our results indicated that seepweed (Suaeda salsa) tidal flats and reed ponds were the favored habitats by cranes in December and January, respectively. The variation partitioning results indicated that plot and landscape factors were the determining factors of crane abundance in December, but plot features were more important in January. Furthermore, the pure and total effects of plot factors, and the combined effects of plot, landscape and spatial factors, increased significantly from December to January. At plot scale, vegetation coverage and road distance were the crucial variables that determine crane abundance in both months. At landscape scale, percentage of reed ponds and percentage of seepweed tidal flats showed a positive independent effect on crane abundance in both months. Percentage of paddy fields was also a significant variable in December, whereas percentage of fishponds was in January. Our study indicated that crane habitat selection and the determining factors changed over time due to food availability and human disturbance (e.g., reed pond and fishpond harvests). Our results encourage the application of partitioning methods in avian ecology because they provide a more in-depth understanding of the importance of different explanatory variables over traditional regression methods. Efforts should be made to strengthen wetland restoration and improve the mitigation of human disturbance in the YNR

Myocardial strain analysis of echocardiography based on deep learning

BackgroundStrain analysis provides more thorough spatiotemporal signatures for myocardial contraction, which is helpful for early detection of cardiac insufficiency. The use of deep learning (DL) to automatically measure myocardial strain from echocardiogram videos has garnered recent attention. However, the development of key techniques including segmentation and motion estimation remains a challenge. In this work, we developed a novel DL-based framework for myocardial segmentation and motion estimation to generate strain measures from echocardiogram videos.MethodsThree-dimensional (3D) Convolutional Neural Network (CNN) was developed for myocardial segmentation and optical flow network for motion estimation. The segmentation network was used to define the region of interest (ROI), and the optical flow network was used to estimate the pixel motion in the ROI. We performed a model architecture search to identify the optimal base architecture for motion estimation. The final workflow design and associated hyperparameters are the result of a careful implementation. In addition, we compared the DL model with a traditional speck tracking algorithm on an independent, external clinical data. Each video was double-blind measured by an ultrasound expert and a DL expert using speck tracking echocardiography (STE) and DL method, respectively.ResultsThe DL method successfully performed automatic segmentation, motion estimation, and global longitudinal strain (GLS) measurements in all examinations. The 3D segmentation has better spatio-temporal smoothness, average dice correlation reaches 0.82, and the effect of target frame is better than that of previous 2D networks. The best motion estimation network achieved an average end-point error of 0.05 ± 0.03 mm per frame, better than previously reported state-of-the-art. The DL method showed no significant difference relative to the traditional method in GLS measurement, Spearman correlation of 0.90 (p < 0.001) and mean bias −1.2 ± 1.5%.ConclusionIn conclusion, our method exhibits better segmentation and motion estimation performance and demonstrates the feasibility of DL method for automatic strain analysis. The DL approach helps reduce time consumption and human effort, which holds great promise for translational research and precision medicine efforts