The Degasperis-Procesi equation with self-consistent sources

The Degasperis-Procesi equation with self-consistent sources(DPESCS) is derived. The Lax representation and the conservation laws for DPESCS are constructed. The peakon solution of DPESCS is obtained.Comment: 15 page

SUMOylation stabilizes sister kinetochore biorientation to allow timely anaphase

During mitosis, sister chromatids attach to microtubules from opposite poles, called biorientation. Sister chromatid cohesion resists microtubule forces, generating tension, which provides the signal that biorientation has occurred. How tension silences the surveillance pathways that prevent cell cycle progression and correct erroneous kinetochore-microtubule attachments remains unclear. Here we show that SUMOylation dampens error correction to allow stable sister kinetochore biorientation and timely anaphase onset. The Siz1/Siz2 SUMO ligases modify the pericentromere-localized shugoshin (Sgo1) protein before its tension-dependent release from chromatin. Sgo1 SUMOylation reduces its binding to protein phosphatase 2A (PP2A), and weakening of this interaction is important for stable biorientation. Unstable biorientation in SUMO-deficient cells is associated with persistence of the chromosome passenger complex (CPC) at centromeres, and SUMOylation of CPC subunit Bir1 also contributes to timely anaphase onset. We propose that SUMOylation acts in a combinatorial manner to facilitate dismantling of the error correction machinery within pericentromeres and thereby sharpen the metaphase-anaphase transition

The Chinese pine genome and methylome unveil key features of conifer evolution

Conifers dominate the world's forest ecosystems and are the most widely planted tree species. Their giant and complex genomes present great challenges for assembling a complete reference genome for evolutionary and genomic studies. We present a 25.4-Gb chromosome-level assembly of Chinese pine (Pinus tabuliformis) and revealed that its genome size is mostly attributable to huge intergenic regions and long introns with high transposable element (TE) content. Large genes with long introns exhibited higher expressions levels. Despite a lack of recent whole-genome duplication, 91.2% of genes were duplicated through dispersed duplication, and expanded gene families are mainly related to stress responses, which may underpin conifers' adaptation, particularly in cold and/or arid conditions. The reproductive regulation network is distinct compared with angiosperms. Slow removal of TEs with high-level methylation may have contributed to genomic expansion. This study provides insights into conifer evolution and resources for advancing research on conifer adaptation and development

The Emission Reduction Effect of Financial Agglomeration under China’s Carbon Peak and Neutrality Goals

The existing literature on the influencing factors of carbon emissions ignores the relationship between financial agglomeration and carbon emissions. Based on the analysis of the emission reduction history of major countries, this paper mainly uses the provincial-level data of China from 2002 to 2018 to explore the impact of financial agglomeration on carbon emissions. The conclusions are as follows: (1) China lacks carbon tax policies; there are many drawbacks in the carbon trading market, and a “bottom-up” voluntary emission reduction mechanism has not been formed. (2) China’s carbon emissions and financial development are characterized by spatial agglomeration. (3) Financial agglomeration can reduce carbon emissions. In central China, the low-carbon region, and the pilot regions for carbon trading, financial agglomeration has a greater impact on reducing emissions. (4) Financial agglomeration can reduce emissions by reducing the proportion of the secondary industry and increasing the proportion of the third industry. (5) Financial agglomeration can still lower carbon emissions when the spacing effect is taken into account. Finally, according to the conclusion, this paper puts forward relevant suggestions to help China reduce carbon emissions

Classification of brain disease in magnetic resonance images using two-stage local feature fusion.

BACKGROUND:Many classification methods have been proposed based on magnetic resonance images. Most methods rely on measures such as volume, the cerebral cortical thickness and grey matter density. These measures are susceptible to the performance of registration and limited in representation of anatomical structure. This paper proposes a two-stage local feature fusion method, in which deformable registration is not desired and anatomical information is represented from moderate scale. METHODS:Keypoints are firstly extracted from scale-space to represent anatomical structure. Then, two kinds of local features are calculated around the keypoints, one for correspondence and the other for representation. Scores are assigned for keypoints to quantify their effect in classification. The sum of scores for all effective keypoints is used to determine which group the test subject belongs to. RESULTS:We apply this method to magnetic resonance images of Alzheimer's disease and Parkinson's disease. The advantage of local feature in correspondence and representation contributes to the final classification. With the help of local feature (Scale Invariant Feature Transform, SIFT) in correspondence, the performance becomes better. Local feature (Histogram of Oriented Gradient, HOG) extracted from 16×16 cell block obtains better results compared with 4×4 and 8×8 cell block. DISCUSSION:This paper presents a method which combines the effect of SIFT descriptor in correspondence and the representation ability of HOG descriptor in anatomical structure. This method has the potential in distinguishing patients with brain disease from controls

An Intelligent Networked Car-Hailing System Based on the Multi Sensor Fusion and UWB Positioning Technology under Complex Scenes Condition

In order to solve the problems of difficulty and long times to pick up cars in complex traffic scenes, this paper proposes an intelligent networked car-hailing system in complex scenes based on multi sensor fusion and Ultra-Wide-Band (UWB) technology. UWB positioning technology is adopted in the system, and the positioning data is optimized by the untraceable Kalman filter algorithm. Based on the environment perception technology of multi sensor fusion, such as machine vision and laser radar technology, an anti-collision warning algorithm was proposed in the process of car-hailing, which improved the safety factor of car-hailing. When the owner enters the parking lot, the intelligent vehicle can automatically locate the owner’s position and drive to the owner without human intervention, which provides a new idea for the development of intelligent networked vehicles and effectively improves the navigation accuracy and intelligence of intelligent vehicles

3D volume is represented by 2D features.

<p>The brain is sliced along 3 orientations to obtain 2D images, and SIFT features are extracted from 2D images.</p