A Dufort-Frankel Difference Scheme for Two-Dimensional Sine-Gordon Equation

A standard Crank-Nicolson finite-difference scheme and a Dufort-Frankel finite-difference scheme are introduced to solve two-dimensional damped and undamped sine-Gordon equations. The stability and convergence of the numerical methods are considered. To avoid solving the nonlinear system, the predictor-corrector techniques are applied in the numerical methods. Numerical examples are given to show that the numerical results are consistent with the theoretical results

Multiple Minor QTLs Are Responsible for Fusarium Head Blight Resistance in Chinese Wheat Landrace Haiyanzhong

Citation: Cai, Jin, Shan Wang, Tao Li, Guorong Zhang, and Guihua Bai. “Multiple Minor QTLs Are Responsible for Fusarium Head Blight Resistance in Chinese Wheat Landrace Haiyanzhong.” Edited by Maoteng Li. PLOS ONE 11, no. 9 (September 27, 2016): e0163292. https://doi.org/10.1371/journal.pone.0163292.Fusarium head blight (FHB), caused by Fusarium graminearum Schwabe, is a devastating disease in wheat (Triticum aestivum L.). Use of host resistance is one of the most effective strategies to minimize the disease damage. Haiyanzhong (HYZ) is a Chinese wheat landrace that shows a high level of resistance to FHB spread within a spike (type II resistance). To map the quantitative trait loci (QTLs) in HYZ and identify markers tightly linked to the QTLs for FHB resistance, a population of 172 recombinant inbred lines (RILs) from a cross between HYZ and Wheaton (FHB susceptible) was genotyped using simple sequence repeats (SSRs) and single-nucleotide polymorphisms (SNPs) derived from genotyping-bysequencing (GBS), and evaluated for percentage of symptomatic spikelets (PSSs) per spike in three greenhouse experiments. Six QTLs for type II resistance were identified in HYZ, indicating that multiple minor QTLs together can provide a high level of FHB resistance in wheat. The QTL with the largest effect on FHB resistance was mapped on the chromosome arm 5AS, and the other five from HYZ were mapped on the chromosomes 6B, 7D, 3B, 4B and 4D. In addition, two QTLs from Wheaton were mapped on 2B. Critical SNPs linked to the QTLs on chromosomes 5A, 6B, and 2B were converted into KBioscience competitive allele-specific PCR (KASP) assays, which can be used for marker-assisted selection (MAS) to pyramid these QTLs in whea

Gold nanocages covered with thermally-responsive polymers for controlled release by high-intensity focused ultrasound

This paper describes the use of Au nanocages covered with smart, thermally-responsive polymers for controlled release with high-intensity focused ultrasound (HIFU). HIFU is a highly precise medical procedure that uses focused ultrasound to heat and destroy pathogenic tissue rapidly and locally in a non-invasive or minimally invasive manner. The released dosage could be remotely controlled by manipulating the power of HIFU and/or the duration of exposure. We demonstrated localized release within the focal volume of HIFU by using gelatin phantom samples containing dye-loaded Au nanocages. By placing chicken breast tissues on top of the phantoms, we further demonstrated the feasibility of this system for controlled release at depths up to 30 mm. Because it can penetrate more deeply into soft tissues than near-infrared light, HIFU is a potentially more effective external stimulus for rapid, on-demand drug release

Experimental study of the vortex-induced vibration of marine risers under middle flow

A considerable number of studies for vortex induced vibration (VIV) under uniform flow have been performed. However, investigation of VIV under middle flow is scarce. An experiment for VIV under middle flow was conducted in a deep-water offshore basin. Various measurements were obtained by the fiber Bragg grating strain sensors placed on the riser, and VIV under the effect of middle flow with was investigated. Results show that the riser vibrates at different order natural frequencies along the water depth in the CF and IL directions appearing as the multi-frequencies under middle flow. The variation vortex shedding frequencies along the riser under middle flow may generate different wake modes and vibration modals as the corresponding vortex shedding frequencies approach the riser natural frequencies. The dominant vibration frequency of the entire riser is consistent, and determined by high order natural frequency and the corresponding closing vortex shedding frequencies under the middle flow. Meanwhile, the vibration modal under middle flow appears multi-modals and other lower modal have effect on riser vibration. The VIV mechanism under middle flow possesses some aspects similar to those of uniform flow and several unique features

Uncovering shape signatures of resting‐state functional connectivity by geometric deep learning on Riemannian manifold

Functional neural activities manifest geometric patterns, as evidenced by the evolving network topology of functional connectivities (FC) even in the resting state. In this work, we propose a novel manifold-based geometric neural network for functional brain networks (called "Geo-Net4Net" for short) to learn the intrinsic low-dimensional feature representations of resting-state brain networks on the Riemannian manifold. This tool allows us to answer the scientific question of how the spontaneous fluctuation of FC supports behavior and cognition. We deploy a set of positive maps and rectified linear unit (ReLU) layers to uncover the intrinsic low-dimensional feature representations of functional brain networks on the Riemannian manifold taking advantage of the symmetric positive-definite (SPD) form of the correlation matrices. Due to the lack of well-defined ground truth in the resting state, existing learning-based methods are limited to unsupervised methodologies. To go beyond this boundary, we propose to self-supervise the feature representation learning of resting-state functional networks by leveraging the task-based counterparts occurring before and after the underlying resting state. With this extra heuristic, our Geo-Net4Net allows us to establish a more reasonable understanding of resting-state FCs by capturing the geometric patterns (aka. spectral/shape signature) associated with resting states on the Riemannian manifold. We have conducted extensive experiments on both simulated data and task-based functional resonance magnetic imaging (fMRI) data from the Human Connectome Project (HCP) database, where our Geo-Net4Net not only achieves more accurate change detection results than other state-of-the-art counterpart methods but also yields ubiquitous geometric patterns that manifest putative insights into brain function

Prior knowledge-based deep learning method for indoor object recognition and application

Indoor object recognition is a key task for indoor navigation by mobile robots. Although previous work has produced impressive results in recognizing known and familiar objects, the research of indoor object recognition for robot is still insufficient. In order to improve the detection precision, our study proposed a prior knowledge-based deep learning method aimed to enable the robot to recognize indoor objects on sight. First, we integrate the public Indoor dataset and the private frames of videos (FoVs) dataset to train a convolutional neural network (CNN). Second, mean images, which are used as a type of colour knowledge, are generated for all the classes in the Indoor dataset. The distance between every mean image and the input image produces the class weight vector. Scene knowledge, which consists of frequencies of occurrence of objects in the scene, is then employed as another prior knowledge to determine the scene weight. Finally, when a detection request is launched, the two vectors together with a vector of classification probability instigated by the deep model are multiplied to produce a decision vector for classification. Experiments show that detection precision can be improved by employing the prior colour and scene knowledge. In addition, we applied the method to object recognition in a video. The results showed potential application of the method for robot vision

Effect of Diquat on gut health: molecular mechanisms, toxic effects, and protective strategies

Diquat is a widely used bipyridyl herbicide that is extensively applied in agricultural production and water management due to its high efficacy in weed control. However, its environmental persistence and the toxic effects it induces have raised widespread concern. Studies show that Diquat primarily enters the body through the digestive tract, leading to poisoning. The core mechanism of its toxicity involves reactive oxygen species (ROS)-induced oxidative stress, which not only directly damages the intestinal barrier function but also exacerbates inflammation and systemic toxicity by disrupting the balance of the gut microbiota and the normal production of metabolic products. This review systematically summarizes the physicochemical properties of Diquat, with a focus on analyzing the mechanisms by which it damages the gut tissue structure, barrier function, and microbiota after digestive tract exposure. The aim is to provide theoretical support for a deeper understanding of Diquat’s toxic mechanisms and its digestive tract-centered toxic characteristics, laying a scientific foundation for the development of effective interventions and protective strategies against its toxicity

One stage lesion detection based on 3D context convolutional neural networks

Abstract(#br)Lesion detection from Computed Tomography (CT) scans is a challenge because non-lesions and true lesions always have similar appearances. Therefore, the performance of mainstream 2D image-based object detection algorithms is not promising since the texture and shape of inner-classes are always different. To detect lesions, we propose a novel deep convolutional feature fusion scheme, 3D Context Feature Fusion (3DCFF). Motivated by state-of-the-art object detection algorithms, we use a one-stage framework, rather than a Region Proposal Network, to extract lesions. In addition, because 3D context provides texture, contour, and shape information that are helpful for generating distinguishable lesion features, 3D context is used as the input for the proposed network. Furthermore, the network adopts a multi-resolution fusion scheme among different scales of feature maps. Results of experiments, conducted with the Deeplesion database, show that the proposed 3DCFF performs better and faster than state-of-the-art algorithms, such as Faster R-CNN, RetinaNet, and 3DCE