PVI-DSO: Leveraging Planar Regularities for Direct Sparse Visual-Inertial Odometry

The monocular Visual-Inertial Odometry (VIO) based on the direct method can leverage all the available pixels in the image to estimate the camera motion and reconstruct the environment. The denser map reconstruction provides more information about the environment, making it easier to extract structure and planar regularities. In this paper, we propose a monocular direct sparse visual-inertial odometry, which exploits the plane regularities (PVI-DSO). Our system detects coplanar information from 3D meshes generated from 3D point clouds and uses coplanar parameters to introduce coplanar constraints. In order to reduce computation and improve compactness, the plane-distance cost is directly used as the prior information of plane parameters. We conduct ablation experiments on public datasets and compare our system with other state-of-the-art algorithms. The experimental results verified leveraging the plane information can improve the accuracy of the VIO system based on the direct method

Neural Membrane Mutual Coupling Characterisation Using Entropy-Based Iterative Learning Identification

This paper investigates the interaction phenomena of the coupled axons while the mutual coupling factor is presented as a pairwise description. Based on the Hodgkin-Huxley model and the coupling factor matrix, the membrane potentials of the coupled myelinated/unmyelinated axons are quantified which implies that the neural coupling can be characterised by the presented coupling factor. Meanwhile the equivalent electric circuit is supplied to illustrate the physical meaning of this extended model. In order to estimate the coupling factor, a data-based iterative learning identification algorithm is presented where the Rényi entropy of the estimation error has been minimised. The convergence of the presented algorithm is analysed and the learning rate is designed. To verified the presented model and the algorithm, the numerical simulation results indicate the correctness and the effectiveness. Furthermore, the statistical description of the neural coupling, the approximation using ordinary differential equation, the measurement and the conduction of the nerve signals are discussed respectively as advanced topics. The novelties can be summarised as follows: 1) the Hodgkin-Huxley model has been extended considering the mutual interaction between the neural axon membranes, 2) the iterative learning approach has been developed for factor identification using entropy criterion, and 3) the theoretical framework has been established for this class of system identification problems with convergence analysis

The chemical profiling of Salvia plebeia during different growth periods and the biosynthesis of its main flavonoids ingredients

Salvia plebeia (Lamiaceae) is a valuable medicinal plant widely distributed across Asia and Oceania. However, the composition and accumulation patterns of its active ingredients in different organs during the growth and their biosynthetic mechanism remain unknown. Therefore, we conducted metabolite profiling, transcriptomic analysis, and biological functional verification to explore the distribution, accumulation, and biosynthesis mechanisms of flavonoids in S. plebeia. We identified 70 metabolites including 46 flavonoids, 16 phenolic acids, seven terpenoids, and one organic acid, of which 21 were previously unreported in S. plebeia. Combining metabolomic-transcriptomic analysis and biological functional verification, we identified the key genes involved in biosynthesis of its main active ingredients, hispidulin and homoplantaginin, including SpPAL, SpC4H, Sp4CL2, Sp4CL5, SpCHS1, SpCHI, SpFNS, SpF6H1, SpF6OMT1, SpF6OMT2, SpUGT1, SpUGT2, and SpUGT3. Using the identified genes, we reconstructed the hispidulin and homoplantaginin biosynthesis pathways in Escherichia coli, and obtained a yield of 5.33 and 3.86 mg/L for hispidulin and homoplantaginin, respectively. Our findings provide valuable insights into the changes in chemical components in different organs of S. plebeia during different growth and harvest stages and establishes a foundation for identifying and synthesizing its active components

Atrial Fibrillation Follow-up Investigation to Recover Memory and Learning Trial (AFFIRMING): Rationale and Design of a Multi-center, Double-blind, Randomized Controlled Trial

Background: People with atrial fibrillation (AF) have elevated risk of developing cognitive impairment. At present, there is a dearth of randomized controlled trials investigating cognitive impairment management in patients with AF. The Atrial Fibrillation Follow-up Investigation to Recover Memory and learning (AFFIRMING) study is aimed at evaluating the potential for computerized cognitive training to improve cognitive function in patients with AF. Methods: The study is a multi-center, double-blind, randomized controlled study using a 1:1 parallel design. A total of 200 patients with AF and mild cognitive decline without dementia are planned to be recruited. The intervention group will use the adaptive training software with changes in difficulty, whereas the positive control group will use basic training software with minimal or no variation in difficulty level. At the end of 12 weeks, the participants will be unblinded, and the positive control group will stop training. The intervention group will be rerandomized 1:1 to stop training or continue training. All participants will be followed up until 24 weeks. The primary endpoint is the proportion of the improvement of the global cognitive function at week 12 compared with baseline, using the Basic Cognitive Ability Test (BCAT)

GWAS Identifies Novel Susceptibility Loci on 6p21.32 and 21q21.3 for Hepatocellular Carcinoma in Chronic Hepatitis B Virus Carriers

Genome-wide association studies (GWAS) have recently identified KIF1B as susceptibility locus for hepatitis B virus (HBV)–related hepatocellular carcinoma (HCC). To further identify novel susceptibility loci associated with HBV–related HCC and replicate the previously reported association, we performed a large three-stage GWAS in the Han Chinese population. 523,663 autosomal SNPs in 1,538 HBV–positive HCC patients and 1,465 chronic HBV carriers were genotyped for the discovery stage. Top candidate SNPs were genotyped in the initial validation samples of 2,112 HBV–positive HCC cases and 2,208 HBV carriers and then in the second validation samples of 1,021 cases and 1,491 HBV carriers. We discovered two novel associations at rs9272105 (HLA-DQA1/DRB1) on 6p21.32 (OR = 1.30, P = 1.13×$10^{−19}$) and rs455804 (GRIK1) on 21q21.3 (OR = 0.84, P = 1.86×$10^{−8}$), which were further replicated in the fourth independent sample of 1,298 cases and 1,026 controls (rs9272105: OR = 1.25, P = 1.71×$10^{−4}$; rs455804: OR = 0.84, P = 6.92×$10^{−3}$). We also revealed the associations of HLA-DRB1*0405 and 0901*0602, which could partially account for the association at rs9272105. The association at rs455804 implicates GRIK1 as a novel susceptibility gene for HBV–related HCC, suggesting the involvement of glutamate signaling in the development of HBV–related HCC

Introduction to Special Issue - In-depth study of air pollution sources and processes within Beijing and its surrounding region (APHH-2 Beijing)

Abstract. The Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-Beijing) programme is an international collaborative project focusing on understanding the sources, processes and health effects of air pollution in the Beijing megacity. APHH-Beijing brings together leading China and UK research groups, state-of-the-art infrastructure and air quality models to work on four research themes: (1) sources and emissions of air pollutants; (2) atmospheric processes affecting urban air pollution; (3) air pollution exposure and health impacts; and (4) interventions and solutions. Themes 1 and 2 are closely integrated and support Theme 3, while Themes 1-3 provide scientific data for Theme 4 to develop cost-effective air pollution mitigation solutions. This paper provides an introduction to (i) the rationale of the APHH-Beijing programme, and (ii) the measurement and modelling activities performed as part of it. In addition, this paper introduces the meteorology and air quality conditions during two joint intensive field campaigns - a core integration activity in APHH-Beijing. The coordinated campaigns provided observations of the atmospheric chemistry and physics at two sites: (i) the Institute of Atmospheric Physics in central Beijing, and (ii) Pinggu in rural Beijing during 10 November – 10 December 2016 (winter) and 21 May- 22 June 2017 (summer). The campaigns were complemented by numerical modelling and automatic air quality and low-cost sensor observations in the Beijing megacity. In summary, the paper provides background information on the APHH-Beijing programme, and sets the scene for more focussed papers addressing specific aspects, processes and effects of air pollution in Beijing

Digital Finance, Environmental Regulation, and Green Technology Innovation: An Empirical Study of 278 Cities in China

Digital finance provides a premises guarantee for green technology innovation, and effective environmental regulation helps to achieve green and sustainable development. This article selects Chinese urban panel data from 2011 to 2019 to explore the impact mechanism of the influence of digital finance and environmental regulation on the innovation capacity of green science and technology. It is found that extensive financing channels and the strong information-matching ability of digital finance have a significant promoting effect on local green science and technology innovation. Moreover, government environmental regulation not only facilitates the development of green technology innovation locally and in nearby regions, but also strengthens the utility of digital finance in driving green science and technology innovation. Further research found that the influence of digital finance and environmental regulation on the ability of green science and technology innovation has regional heterogeneity, and only digital finance in Central China can promote green science and technology innovation in both local and adjacent areas. Therefore, the government should continue to promote the development of digital finance, optimize environmental regulations by increasing environmental protection subsidies and creating a green innovation environment, and further stimulate willingness to innovate green technologies. At the same time, it is also important to note the coordinated development and governance with neighboring regional governments

Digital Finance, Environmental Regulation, and Green Technology Innovation: An Empirical Study of 278 Cities in China

Digital finance provides a premises guarantee for green technology innovation, and effective environmental regulation helps to achieve green and sustainable development. This article selects Chinese urban panel data from 2011 to 2019 to explore the impact mechanism of the influence of digital finance and environmental regulation on the innovation capacity of green science and technology. It is found that extensive financing channels and the strong information-matching ability of digital finance have a significant promoting effect on local green science and technology innovation. Moreover, government environmental regulation not only facilitates the development of green technology innovation locally and in nearby regions, but also strengthens the utility of digital finance in driving green science and technology innovation. Further research found that the influence of digital finance and environmental regulation on the ability of green science and technology innovation has regional heterogeneity, and only digital finance in Central China can promote green science and technology innovation in both local and adjacent areas. Therefore, the government should continue to promote the development of digital finance, optimize environmental regulations by increasing environmental protection subsidies and creating a green innovation environment, and further stimulate willingness to innovate green technologies. At the same time, it is also important to note the coordinated development and governance with neighboring regional governments

Dynamic responses and adiabatic shear behaviors of TC17 and TC4 alloy forgings

Titanium alloys are widely used in the manufacture of aero-engine blisks due to their low density, high strength, and excellent performance at medium and high temperatures. However, there are few studies on dynamic mechanical properties and adiabatic shear sensitivities of titanium alloy forgings for blisks. In this work, the dynamic mechanical properties of forged TC17 and TC4 alloys at high strain rates were examined by SHPB apparatus, and OM, SEM, EBSD were used to study the adiabatic shear behaviors of the two kinds of alloy. As the strain rate increases, the strength of both alloys increases, thus exhibiting the strain rate strengthening effect. At the same strain rate, TC4 alloy exhibits higher plastic strain and dynamic absorbed energy than those of TC17 alloy. TC17 alloy obtains a basket-weave microstructure after β forging, in which lath α-phases and residual β phases form more phase interfaces. ASBs tend to form at phase interfaces, which lead to a tendency for ASBs to bifurcate during propagating processes. TC4 alloy obtains a bimodal microstructure after α+β forging, and equiaxed primary α-phases show good ductility, which improve the dynamic plastic deformation ability. The regular arrangement of secondary α-phases results in fewer phase interfaces, leading to the difficulty in bifurcation of ASBs during propagating processes. Under dynamic interrupted compression conditions, ASBs in TC17 alloy are occurred earlier, and the localization energy of ASBs is low. Therefore, TC17 alloy has higher adiabatic shear sensitivity, and adiabatic shear sensitivities of both alloys increase with the increase of strain rates