Identification and Expression Analysis of Abscisic Acid Signal Transduction Genes in Hemp Seeds

Abscisic acid(ABA)is involved in regulating diverse biological processes,but its signal transduction genes and roles in hemp seed germination are not well known.Here,the ABA signaling pathway members,PYL,PP2C and SnRK2 gene families,were identified from the hemp reference genome,including 7 CsPYL(pyrab-actin resistance1-like,ABA receptor),8 CsPP2CA(group A protein phosphatase 2c),and 7 CsSnRK2(sucrose nonfermenting1-related protein kinase 2).The content of ABA in hemp seeds in germination stage is lower than that in non-germination stage.Exogenous ABA(1 or 10μM)treatment had a significant regulatory effect on the selected PYL,PP2C,SnRK2 gene families.CsAHG3 and CsHAI1 were most significantly affected by exogenous ABA treatment.Yeast two-hybrid experiments were performed to reveal that CsPYL5,CsSnRK2.2,and CsSnRK2.3 could interact with CsPP2CA7 and demonstrate that this interaction was ABA-independent.Our results indicated that CsPYL5,CsSnRK2.2,CsSnRK2.3 and CsPP2CA7 might involve in the ABA signaling transduction pathway of hemp seeds during the hemp seed germination stages.This study suggested that novel genetic views can be brought into investigation of ABA signaling pathway in hemp seeds and lay the foundation for further exploration of the mechanism of hemp seed germination.

Model of decentralized cross-chain energy trading for power systems

With the development of the energy Internet, more distributed generators are connected to the power grid, resulting in numerous heterogeneous energy networks. However, different energy networks cannot perform efficient energy trading in the centralized management mode, this deeply affecting the complementary ability of heterogeneous energy, resulting in the islanded energy phenomenon. In this model, the same energy on the chain is traded within the chain, and the heterogeneous energy on different chains is traded across chains. To trade energy between heterogeneous energy networks more efficiently, the blockchain-based cross-chain model is proposed based on the existing infrastructure. Heterogeneous energy nodes are assigned to different energy sub-chains and cross-chain energy transactions are performed through a relay-chain, which utilizes the improved Boneh–Lynn–Shacham signature scheme consensus algorithm based on the proof-of-stake and practical Byzantine fault tolerance. The experimental simulations on energy trading efficiency, throughput, and security, show its superiority over existing systems. Further, the simulation results provide a reference for the application of cross-chain technology in energy interconnection.

Dichloroacetic acid and rapamycin synergistically inhibit tumor progression

Mammalian target of rapamycin(mTOR)controls cellular anabolism,and mTOR signaling is hyperactive in most cancer cells.As a result,inhibition of mTOR signaling benefits cancer patients.Rapamycin is a US Food and Drug Administration(FDA)-approved drug,a specific mTOR complex 1(mTORC1)inhibitor,for the treatment of several different types of cancer.However,rapamycin is reported to inhibit cancer growth rather than induce apoptosis.Pyruvate dehydrogenase complex(PDHc)is the gatekeeper for mitochondrial pyruvate oxidation.PDHc inactivation has been observed in a number of cancer cells,and this alteration protects cancer cells from senescence and nicotinamide adenine dinucleotide(NAD^(+))exhaustion.In this paper,we describe our finding that rapamycin treatment promotes pyruvate dehydrogenase E1 subunit alpha 1(PDHA1)phosphorylation and leads to PDHc inactivation dependent on mTOR signaling inhibition in cells.This inactivation reduces the sensitivity of cancer cells'response to rapamycin.As a result,rebooting PDHc activity with dichloroacetic acid(DCA),a pyruvate dehydrogenase kinase(PDK)inhibitor,promotes cancer cells'susceptibility to rapamycin treatment in vitro and in vivo.

Morphology and composition of particles emitted from a port fuel injection gasoline vehicle under real-world driving test cycles

Traffic vehicles, many of which are powered by port fuel injection(PFI) engines, are major sources of particulate matter in the urban atmosphere. We studied particles from the emission of a commercial PFI-engine vehicle when it was running under the states of cold start, hot start, hot stabilized running, idle and acceleration, using a transmission electron microscope and an energy-dispersive X-ray detector. Results showed that the particles were mainly composed of organic, soot, and Ca-rich particles, with a small amount of S-rich and metal-containing particles, and displayed a unimodal size distribution with the peak at 600 nm. The emissions were highest under the cold start running state, followed by the hot start, hot stabilized, acceleration, and idle running states. Organic particles under the hot start and hot stabilized running states were higher than those of other running states. Soot particles were highest under the cold start running state. Under the idle running state, the relative number fraction of Ca-rich particles was high although their absolute number was low. These results indicate that PFI-engine vehicles emit substantial primary particles,which favor the formation of secondary aerosols via providing reaction sites and reaction catalysts, as well as supplying soot, organic, mineral and metal particles in the size range of the accumulation mode. In addition, the contents of Ca, P, and Zn in organic particles may serve as fingerprints for source apportionment of particles from PFI-engine vehicles.

Characterization of coal burning-derived individual particles emitted from an experimental domestic stove

Coal combustion in the domestic stoves, which is common in most parts of the Chinese countryside, can release harmful substances into the air and cause health issues. In this study, particles emitted from laboratory stove combustion of the raw powder coals were analyzed for morphologies and chemical compositions by using transmission electron microscopy （TEM） coupled with energy-dispersive X-ray spectrometry （EDX）. The coal burning-derived individual particles were classified into two groups： carbonaceous particles （including soot aggregates and organic particles） and non-carbonaceous particles （including sulfate, mineral and metal particles）. The non-carbonaceous particles, which constituted a majority of the coal burning-derived emissions, were subdivided into Si-rich, S-rich, K-rich, Ca-rich, and Fe-rich particles according to the elemental compositions. The Si-rich, S-rich and K-rich particles are commonly observed in the coal burning emission. The proportions for particles of different types exhibit obvious coal-issue dependence. Burning of coal with high ash yield could emit more non-carbonaceous particles, and burning of coal with high sulfur content can emit more S-rich particles. By comparing the S-rich particles from this coal burning experiment with those in the atmosphere, we draw a conclusion that some S-rich particles in the atmosphere in China could be mainly sourced from coal combustion.

Distribution of rare earth elements in PM10 emitted from burning coals and soil-mixed coal briquettes

Emission from burning coals is one of the major sources of the airborne particles in China.We carried out a study on the rare earth elements(REEs)in the inhalable particulate matter(PM10)emitted from burning coals and soil-coal honeycomb briquettes with different volatile contents and ash yields in a combustion-dilution system.Gravimetric analysis indicates that the equivalent mass concentration of the PM10 emitted from burning the coals is higher than that emitted from burning the briquettes.The ICP-MS analysis indicates that the contents of total REEs in the coal-burning PM10 are lower than those in the briquetteburning PM10.In addition,the contents of the light rare earth elements(LREEs)are higher than those of the heavy rare earth elements(HREEs)in the PM10 emitted from burning the coals and briquettes,demonstrating that the REEs in both the coal-burning and briquetteburning PM10 are dominated by LREEs.The higher contents of total REEs and LREEs in the coal-burning PM10 are associated with the higher ash yields and lower volatile contents in the raw coals.A comparative analysis indicates that the La/Sm ratios in the PM10 emitted from burning the coals and briquettes,being lower than 2,are lower than those in the particles from gasoline-powered vehicle emission.

Element-wise representations with ECNet for material property prediction and applications in high-entropy alloys

When developing deep learning models for accurate property prediction,it is sometimes overlooked that some material physical properties are insensitive to the local atomic environment.Here,we propose the elemental convolution neural networks(ECNet)to obtain more general and global element-wise representations to accurately model material properties.It shows better prediction in properties like band gaps,refractive index,and elastic moduli of crystals.To explore its application on high-entropy alloys(HEAs),we focus on the FeNiCoCrMn/Pd systems based on the data of DFT calculation.The knowledge from less-principal element alloys can enhance performance in HEAs by transfer learning technique.Besides,the element-wise features from the parent model as universal descriptors retain good accuracy at small data limits.Using this framework,we obtain the concentration-dependent formation energy,magnetic moment and local displacement in some sub-ternary and quinary systems.The results enriched the physics of those high-entropy alloys.