A conserved but plant-specific CDK-mediated regulation of DNA replication protein A2 in the precise control of stomatal terminal division

The R2R3-MYB transcription factor FOUR LIPS (FLP) controls the stomatal terminal division through transcriptional repression of the cell cycle genes CYCLIN-DEPENDENT KINASE (CDK) B1s (CDKB1s), CDKA; 1, and CYCLIN A2s (CYCA2s). We mutagenized the weak mutant allele flp-1 seeds with ethylmethane sulfonate and screened out a flp-1 suppressor 1 (fsp1) that suppressed the flp-1 stomatal cluster phenotype. FSP1 encodes RPA2a subunit of Replication Protein A (RPA) complexes that play important roles in DNA replication, recombination, and repair. Here, we show that FSP1/RPA2a functions together with CDKB1s and CYCA2s in restricting stomatal precursor proliferation, ensuring the stomatal terminal division and maintaining a normal guard-cell size and DNA content. Furthermore, we provide direct evidence for the existence of an evolutionarily conserved, but plant-specific, CDK-mediated RPA regulatory pathway. Serine-11 and Serine-21 at the N terminus of RPA2a are CDK phosphorylation target residues. The expression of the phosphorylation-mimic variant RPA2a(S11,21/D) partially complemented the defective cell division and DNA damage hypersensitivity in cdkb1;1 1;2 mutants. Thus, our study provides a mechanistic understanding of the CDK-mediated phosphorylation of RPA in the precise control of cell cycle and DNA repair in plants

A Study on Public Adoption of Robo-Taxis in China

Autonomous vehicles (AVs) have been reported to improve road safety, reduce traffic congestion, and increase urban mobility. However, the high price of AVs is currently a challenge for most consumers. Robo-taxi services, with ride-sharing services and AVs, are regarded as a good approach to solving this problem. As some companies have started testing Robo-taxis on the actual road, it has become important to investigate public adoption of Robo-taxi services before they are more widely introduced to the market. This study aims to explain and predict users’ acceptance of Robo-taxis by extending the Technology Acceptance Model by including the construct of social influence. Data were collected from an online survey in China and analyzed using linear regression models. The results indicate that perceived usefulness, perceived ease of use, and social influence have significant positive correlations with people’s behavior intentions to use Robo-taxis. Perceived ease of use further has an indirect effect on intention to use via perceived usefulness. The results of this study can serve as good references for policymakers, operators, and future transport researchers. Document type: Articl

A Brewster route to Cherenkov detectors.

Cherenkov detectors enable a valuable tool to identify high-energy particles. However, their sensitivity and momentum coverage are limited by the refractive index of host materials. Especially, identifying particles with energy above multiple gigaelectronvolts requires host materials with a near-unity refractive index, which are limited to bulky gas chambers. Overcoming this fundamental material limit is important for future particle detectors yet remains a long-standing challenge. Here, we propose a different paradigm for Cherenkov detectors that utilizes the broadband angular filter made from stacks of variable one-dimensional photonic crystals. Owing to the Brewster effect, the angular filter is transparent only to Cherenkov photons from a precise incident angle. Particle identification is achieved by mapping each Cherenkov angle to the peak-intensity position of transmitted photons in the detection plane. Such angular filtering effect, although decreases the photon number collected in the detection plane, enables the realization of a non-dispersive pseudo refractive index over the entire visible spectrum. Moreover, the pseudo refractive index can be flexibly designed to different values close to unity. Our angular-selective Brewster paradigm offers a feasible solution to implement compact and highly sensitive Cherenkov detectors especially in beam lines with a small angular divergence using regular dielectrics

Mesenchymal stem cells as carriers and amplifiers in CRAd delivery to tumors

<p>Abstract</p> <p>Background</p> <p>Mesenchymal stem cells (MSCs) have been considered to be the attractive vehicles for delivering therapeutic agents toward various tumor diseases. This study was to explore the distribution pattern, kinetic delivery of adenovirus, and therapeutic efficacy of the MSC loading of E1A mutant conditionally replicative adenovirus Adv-Stat3(-) which selectively replicated and expressed high levels of anti-sense Stat3 complementary DNA in breast cancer and melanoma cells.</p> <p>Methods</p> <p>We assessed the release ability of conditionally replicative adenovirus (CRAd) from MSC using crystal violet staining, TCID₅₀assay, and quantitative PCR. In vitro killing competence of MSCs carrying Adv-Stat3(-) toward breast cancer and melanoma was performed using co-culture system of transwell plates. We examined tumor tropism of MSC by Prussian blue staining and immunofluorescence. In vivo killing competence of MSCs carrying Adv-Stat3(-) toward breast tumor was analyzed by comparison of tumor volumes and survival periods.</p> <p>Results</p> <p>Adv-Stat3(-) amplified in MSCs and were released 4 days after infection. MSCs carrying Adv-Stat3(-) caused viral amplification, depletion of Stat3 and its downstream proteins, and led to significant apoptosis in breast cancer and melanoma cell lines. In vivo experiments confirmed the preferential localization of MSCs in the tumor periphery 24 hours after tail vein injection, and this localization was mainly detected in the tumor parenchyma after 72 hours. Intravenous injection of MSCs carrying Adv-Stat3(-) suppressed the Stat3 pathway, down-regulated Ki67 expression, and recruited CD11b-positive cells in the local tumor, inhibiting tumor growth and increasing the survival of tumor-bearing mice.</p> <p>Conclusions</p> <p>These results indicate that MSCs migrate to the tumor site in a time-dependent manner and could be an effective platform for the targeted delivery of CRAd and the amplification of tumor killing effects.</p

Exploring the Energy Efficiency of Electric Vehicles with Driving Behavioral Data from a Field Test and Questionnaire

With increasing concerns about urban air quality and carbon emissions, electric vehicles (EVs) have gained popularity in megacities, especially in Europe and Asia. The energy consumption of EVs has subsequently caught researchers’ attention. However, the exploration of energy consumption of EVs has largely focused on people’s revealed driving behavior and rarely touched on their self-perception of driving styles. In this paper, we developed a more human-centric approach, aiming to investigate how the energy efficiency of EVs is shaped by the driving behavior and driving style in the urban scenario from field test data and driving style questionnaires (DSQs). Field tests were carried out on a designated route for a total of 13 drivers in the city of Beijing, where vehicle operation parameters were recorded under both congested and smooth traffic conditions. DSQs were collected from a larger pool of drivers including the field test drivers to be applied to driving style factor analysis. The results of a correlation analysis demonstrate the dynamic interaction between drivers’ revealed behavior and stated driving style under different traffic conditions. We also proposed an energy consumption prediction model with the fusion of collected driving parameters and DSQ data and the result is promising. We hope that this study would draw inspiration for future research on people’s transitioning driving behavior in an electric-mobility era

Fault Detection in Active Magnetic Bearings Using Digital Twin Technology

Active magnetic bearings (AMBs) are widely used in different industries to offer non-contact and high-velocity rotational support. The AMB is prone to failures, which may result in system instability and decreased performance. The efficacy and reliability of magnetic bearings can be significantly affected by failures in the sensor and control systems, leading to system imbalance and possible damage. A digital twin is an advanced technology that has been increasingly used in different industrial fields. It allows for the creation and real-time monitoring of virtual replicas of physical systems. This paper proposes a novel method for fault detection of Active Magnetic Bearings (AMBs) using digital twin technology and a neural network. The digital twin model serves as a virtual representation that accurately replicates the actual AMB system’s efficiency and features, allowing continuous real-time monitoring and detection of faults. The conventional neural network (CNN) is used as the primary tool for identifying faults in the Active Magnetic Bearing (AMB) within a digital twin model. Experiments proved the effectiveness and robustness of the suggested approach method to fault detection in the AMB

Impact of interlayer on the anisotropic multi-layered medium overlaying viscoelastic layer under axisymmetric loading

© 2018 Elsevier Inc. Multi-layered media is the most common structure in artificial and natural surroundings, and it is considered as a good candidate for mathematical model. The anisotropic properties and viscoelastic properties of layer materials have been widely used in multi-layered medium. Furthermore, the interlayer condition between the layers also plays an important role in the mechanical behavior of multi-layered medium. Despite many numerical approaches having been applied in these analyses, only few analytical solution were developed to analyze the effects of these three issues at the same time. The objective of this study is to develop an analytical solution that can be used for analyzing the impact of interlayer conditions on the mechanical behavior of anisotropic multi-layered medium overlaying viscoelastic layer subjected to axisymmetric loading, but the anisotropy property was approximated as transversely isotropy. The interlayer condition between the adjacent layers will also be considered. Details of the mathematical derivation, implementation and verification of the proposed analytical solution were presented. Subsequent numerical results demonstrate that both viscoelastic, transverse isotropy and interlayer condition could substantially contribute to the mechanical behavior of the multi-layered medium

Review of the Effect of Surface Coating Modification on Magnesium Alloy Biocompatibility

Magnesium alloy, as an absorbable and implantable biomaterial, has been greatly developed in the application field of biomaterials in recent years due to its excellent biocompatibility and biomechanics. However, due to the poor corrosion resistance of magnesium alloy in the physiological environment, the degradation rate will be unbalanced, which seriously affects the clinical use. There are two main ways to improve the corrosion resistance of magnesium alloy: one is by adding alloying elements, the other is by surface modification technology. Compared with adding alloy elements, the surface coating modification has the following advantages: (1) The surface coating modification is carried out without changing the matrix elements of magnesium alloy, avoiding the introduction of other elements; (2) The corrosion resistance of magnesium alloy can be improved by relatively simple physical, chemical, or electrochemical improvement. From the perspective of corrosion resistance and biocompatibility of biomedical magnesium alloy materials, this paper summarizes the application and characteristics of six different surface coating modifications in the biomedical magnesium alloy field, including chemical conversion method, micro-arc oxidation method, sol-gel method, electrophoretic deposition, hydrothermal method, and thermal spraying method. In the last section, it looks forward to the development prospect of surface coating modification and points out that preparing modified coatings on the implant surface combined with various modification post-treatment technologies is the main direction to improve biocompatibility and realize clinical functionalization

Prediction of autogenous shrinkage of concretes by support vector machine

Support vector machine (SVM) is firmly based on learning theory and uses regression technique by introducing accuracy insensitive loss function. In this paper, a SVM model for the autogenous shrinkage of concrete mixtures was proposed. The model chose water-to-cementitious material ratio (w/cm), cement content, silica fume percentage, fly ash percentage, total aggregate content, curing temperature, high-range water-reducing admixture (HRWRA) content, and hydration age as input parameters, and the autogenous shrinkage of concrete as the model output. The data set used for training and testing of the SVM model covers the experimental data presented in the existing literature. The developed SVM model was validated using experimental work. The SVM model was compared with the ANN prediction model, the SVM model shows comparable prediction accuracy and could easily be established. In short, the proposed SVM model exhibited excellent capability in predicting the autogenous shrinkage of concrete mixtures. Keywords: Support vector machine, Autogenous shrinkage, Prediction, Concrete mixture