Global Exponential Stability of Delayed Periodic Dynamical Systems

In this paper, we discuss delayed periodic dynamical systems, compare capability of criteria of global exponential stability in terms of various $L^{p}$ ($1\le p<\infty$) norms. A general approach to investigate global exponential stability in terms of various $L^{p}$ ($1\le p<\infty$) norms is given. Sufficient conditions ensuring global exponential stability are given, too. Comparisons of various stability criteria are given. More importantly, it is pointed out that sufficient conditions in terms of $L^{1}$ norm are enough and easy to implement in practice

A Novel F-Box Protein CaF-Box Is Involved in Responses to Plant Hormones and Abiotic Stress in

Abstract: The F-box protein family is characterized by an F-box motif that has been shown to play an important role in regulating various developmental processes and stress responses. In this study, a novel F-box-containing gene was isolated from leaves of pepper cultivar P70 (Capsicum annuum L.) and designated CaF-box. The full-length cDNA is 2088 bp and contains an open reading frame of 1914 bp encoding a putative polypeptide of 638 amino acids with a mass of 67.8 kDa. CaF-box was expressed predominantly in stems and seeds, and the transcript was markedly upregulated in response to cold stress, abscisic acid (ABA) and salicylic acid (SA) treatment, and downregulated under osmotic and heavy metal stress. CaF-box expression was dramatically affected by salt stress, and was rapidly increased for the first hour, then sharply decreased thereafter. In order to further assess the role of CaF-box in the defense response to abiotic stress, a loss-of-function experiment in pepper plants was performed using a virus-induced gene silencing (VIGS) technique. Measurement of thiobarbituric acid reactive substances (TBARS) and electrolyte leakage revealed stronger lipid peroxidation and cell death in the CaF-box-silenced plants than inInt. J. Mol. Sci. 2014, 15 2414 control plants, suggesting CaF-box plays an important role in regulating the defens

Multi-dark-state resonances in cold multi-Zeeman-sublevel atoms

We present our experimental and theoretical studies of multi-dark-state resonances (MDSRs) generated in a unique cold rubidium atomic system with only one coupling laser beam. Such MDSRs are caused by different transition strengths of the strong coupling beam connecting different Zeeman sublevels. Controlling the transparency windows in such electromagnetically induced transparency system can have potential applications in multi-wavelength optical communication and quantum information processing.Comment: 11pages, 4figure

mmBody Benchmark: 3D Body Reconstruction Dataset and Analysis for Millimeter Wave Radar

Millimeter Wave (mmWave) Radar is gaining popularity as it can work in adverse environments like smoke, rain, snow, poor lighting, etc. Prior work has explored the possibility of reconstructing 3D skeletons or meshes from the noisy and sparse mmWave Radar signals. However, it is unclear how accurately we can reconstruct the 3D body from the mmWave signals across scenes and how it performs compared with cameras, which are important aspects needed to be considered when either using mmWave radars alone or combining them with cameras. To answer these questions, an automatic 3D body annotation system is first designed and built up with multiple sensors to collect a large-scale dataset. The dataset consists of synchronized and calibrated mmWave radar point clouds and RGB(D) images in different scenes and skeleton/mesh annotations for humans in the scenes. With this dataset, we train state-of-the-art methods with inputs from different sensors and test them in various scenarios. The results demonstrate that 1) despite the noise and sparsity of the generated point clouds, the mmWave radar can achieve better reconstruction accuracy than the RGB camera but worse than the depth camera; 2) the reconstruction from the mmWave radar is affected by adverse weather conditions moderately while the RGB(D) camera is severely affected. Further, analysis of the dataset and the results shadow insights on improving the reconstruction from the mmWave radar and the combination of signals from different sensors.Comment: ACM Multimedia 2022, Project Page: https://chen3110.github.io/mmbody/index.htm

CT-NeRF: Incremental Optimizing Neural Radiance Field and Poses with Complex Trajectory

Neural radiance field (NeRF) has achieved impressive results in high-quality 3D scene reconstruction. However, NeRF heavily relies on precise camera poses. While recent works like BARF have introduced camera pose optimization within NeRF, their applicability is limited to simple trajectory scenes. Existing methods struggle while tackling complex trajectories involving large rotations. To address this limitation, we propose CT-NeRF, an incremental reconstruction optimization pipeline using only RGB images without pose and depth input. In this pipeline, we first propose a local-global bundle adjustment under a pose graph connecting neighboring frames to enforce the consistency between poses to escape the local minima caused by only pose consistency with the scene structure. Further, we instantiate the consistency between poses as a reprojected geometric image distance constraint resulting from pixel-level correspondences between input image pairs. Through the incremental reconstruction, CT-NeRF enables the recovery of both camera poses and scene structure and is capable of handling scenes with complex trajectories. We evaluate the performance of CT-NeRF on two real-world datasets, NeRFBuster and Free-Dataset, which feature complex trajectories. Results show CT-NeRF outperforms existing methods in novel view synthesis and pose estimation accuracy

A comprehensive review of natural products in rheumatoid arthritis: therapeutic potential and mechanisms

Rheumatoid arthritis (RA) is a classic autoimmune disease caused by a combination of genetic and environmental factors. The multiple and comprehensive pathologies involving the whole body’s immune system and local organs and tissues make it challenging to control or cure them clinically. Fortunately, there are increasing reports that multiple non-toxic or low-toxicity natural products and their derivatives (NP&amp;TDs) have positive therapeutic effects on RA. This review focuses on the potential mechanisms of NP&amp;TDs against RA and aims to provide constructive information for developing rational clinical therapies. Active components of NP&amp;TDs can play therapeutic and palliative roles in RA through multiple biological mechanisms. These mechanisms primarily involve immunosuppressive, anti-inflammatory, autophagic, and apoptotic pathways. Multiple targets- and receptor-coupled signal transduction can directly or indirectly modulates the nuclear transcription factors NF-κB, NFATc1, STAT3, and HIF-1α, which in turn regulate the production of several downstream pro-inflammatory cytokines, chemokines, immunocytes maturation and differentiation, immune complexes, proliferation, and apoptosis regulatory genes. Among these NP&amp;TDs, the tripterygium-type ingredients, the artemisinin-type ingredients, and the paeony-type ingredients have been reported to be the mainstay in treating RA. Mechanistically, immunosuppression and anti-inflammation are still the primary therapeutic mechanisms. Nevertheless, the direct binding targets and pharmacodynamic mechanisms require further in-depth studies

Simultaneous determination of multiple components in rat plasma by UHPLC-sMRM for pharmacokinetic studies after oral administration of Qingjin Yiqi Granules

As a Traditional Chinese Medicine prescription, Qingjin Yiqi Granules (QJYQ) provides an effective treatment for patients recovering from COVID-19. However, the pharmacokinetics characteristics of the main components of QJYQ in vivo are still unknown. An efficacious ultra-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) was developed and validated for the simultaneous determination of 33 components in rat plasma after oral administration of QJYQ. The plasma samples were precipitated with 400 µL methanol/acetonitrile (1/1, v/v) and analyzed in scheduled multiple reaction monitoring mode. The linear relationship of the 33 components was good (r &gt; 0.9928). The lower limit of quantification for 33 components ranged from 0.4–60.5 ng/mL. The average recoveries and matrix effects of the analytes ranged from 72.9% to 115.0% with RSD of 1.4%–15.0%. All inter-day and intra-day RSDs were within 15.0%. After oral administration (3.15 g/kg), the validated approach was effectively applied to the pharmacokinetics of main components of QJYQ. Finally, fifteen main constituents of QJYQ with large plasma exposure were obtained, including baicalin, wogonoside, wogonin, apigenin-7-O-glucuronide, verbenalin, isoferulic acid, hesperidin, liquiritin, harpagide, protocatechuic acid, p-Coumaric acid, ferulic acid, sinapic acid, liquiritin apioside and glycyrrhizic acid. The present research lays a foundation for clarifying the therapeutic material basis of QJYQ and provides a reference for further scientific research and clinical application of QJYQ

GEGA: Graph Convolutional Networks and Evidence Retrieval Guided Attention for Enhanced Document-level Relation Extraction

Document-level relation extraction (DocRE) aims to extract relations between entities from unstructured document text. Compared to sentence-level relation extraction, it requires more complex semantic understanding from a broader text context. Currently, some studies are utilizing logical rules within evidence sentences to enhance the performance of DocRE. However, in the data without provided evidence sentences, researchers often obtain a list of evidence sentences for the entire document through evidence retrieval (ER). Therefore, DocRE suffers from two challenges: firstly, the relevance between evidence and entity pairs is weak; secondly, there is insufficient extraction of complex cross-relations between long-distance multi-entities. To overcome these challenges, we propose GEGA, a novel model for DocRE. The model leverages graph neural networks to construct multiple weight matrices, guiding attention allocation to evidence sentences. It also employs multi-scale representation aggregation to enhance ER. Subsequently, we integrate the most efficient evidence information to implement both fully supervised and weakly supervised training processes for the model. We evaluate the GEGA model on three widely used benchmark datasets: DocRED, Re-DocRED, and Revisit-DocRED. The experimental results indicate that our model has achieved comprehensive improvements compared to the existing SOTA model

Exosome delivery to the testes for dmrt1 suppression: a powerful tool for sex-determining gene studies

Exosomes are endosome-derived extracellular vesicles about 100 nm in diameter. They are emerging as prom ising delivery platforms due to their advantages in biocompatibility and engineerability. However, research into and applications for engineered exosomes are still limited to a few areas of medicine in mammals. Here, we expanded the scope of their applications to sex-determining gene studies in early vertebrates. An integrated strategy for constructing the exosome-based delivery system was developed for efficient regulation of dmrt1, which is one of the most widely used sex-determining genes in metazoans. By combining classical methods in molecular biology and the latest technology in bioinformatics, isomiR-124a was identified as a dmrt1 inhibitor and was loaded into exosomes and a testis-targeting peptide was used to modify exosomal surface for efficient delivery. Results showed that isomiR-124a was efficiently delivered to the testes by engineered exosomes and revealed that dmrt1 played important roles in maintaining the regular structure and function of testis in juvenile fish. This is the first de novo development of an exosome-based delivery system applied in the study of sex determining gene, which indicates an attractive prospect for the future applications of engineered exosomes in exploring more extensive biological conundrums.info:eu-repo/semantics/publishedVersio