SEPALLATA-driven MADS transcription factor tetramerization is required for inner whorl floral organ development.

MADS transcription factors are master regulators of plant reproduction and flower development. The SEPALLATA (SEP) subfamily of MADS transcription factors is required for the development of floral organs and plays roles in inflorescence architecture and development of the floral meristem. SEPALLATAs act as organizers of MADS complexes, forming both heterodimers and heterotetramers in vitro. To date, the MADS complexes characterized in angiosperm floral organ development contain at least 1 SEPALLATA protein. Whether DNA binding by SEPALLATA-containing dimeric MADS complexes is sufficient for launching floral organ identity programs, however, is not clear as only defects in floral meristem determinacy were observed in tetramerization-impaired SEPALLATA mutant proteins. Here, we used a combination of genome-wide-binding studies, high-resolution structural studies of the SEP3/AGAMOUS (AG) tetramerization domain, structure-based mutagenesis and complementation experiments in Arabidopsis (Arabidopsis thaliana) sep1 sep2 sep3 and sep1 sep2 sep3 ag-4 plants transformed with versions of SEP3 encoding tetramerization mutants. We demonstrate that while SEP3 heterodimers can bind DNA both in vitro and in vivo and recognize the majority of SEP3 wild-type-binding sites genome-wide, tetramerization is required not only for floral meristem determinacy but also for floral organ identity in the second, third, and fourth whorls.

Comparative proteomic and metabolomic analyses reveal stress responses of hemp to salinity.

KEY MESSAGE: Integrated omics analyses outline the cellular and metabolic events of hemp plants in response to salt stress and highlight several photosynthesis and energy metabolism related pathways as key regulatory points. Soil salinity affects many physiological processes of plants and leads to crop yield losses worldwide. For hemp, a crop that is valued for multiple aspects, such as its medical compounds, fibre, and seed, a comprehensive understanding of its salt stress responses is a prerequisite for resistance breeding and tailoring its agronomic performance to suit certain industrial applications. Here, we first observed the phenotype of salt-stressed hemp plants and found that under NaCl treatment, hemp plants displayed pronounced growth defects, as indicated by the significantly reduced average height, number of leaves, and chlorophyll content. Next, we conducted comparative proteomics and metabolomics to dissect the complex salt-stress response mechanisms. A total of 314 proteins and 649 metabolites were identified to be differentially behaving upon NaCl treatment. Functional classification and enrichment analysis unravelled that many differential proteins were proteases associated with photosynthesis. Through metabolic pathway enrichment, several energy-related pathways were found to be altered, such as the biosynthesis and degradation of branched-chain amino acids, and our network analysis showed that many ribosomal proteins were involved in these metabolic adaptations. Taken together, for hemp plants, influences on chloroplast function probably represent a major toxic effect of salinity, and modulating several energy-producing pathways possibly through translational regulation is presumably a key protective mechanism against the negative impacts. Our data and analyses provide insights into our understanding of hemp's stress biology and may lay a foundation for future functional genomics studies.

Acoustic Emission Analysis of Corroded Reinforced Concrete Columns under Compressive Loading.

In this work, the failure process of non-corroded and corroded reinforced concrete (RC) columns under eccentric compressive loading is studied using the acoustic emission (AE) technique. The results show that reinforcement corrosion considerably affects the mechanical failure process of RC columns. The corrosion of reinforcement in RC columns leads to highly active AE signals at the initial stage of loading, in comparison to the non-corroded counterparts. Also, a continuous AE hit pattern with a higher number of cumulative hits is observed for corroded RC columns. The spatial distribution and evolution of AE events indicate that the reinforcement corrosion noticeably accelerates the initiation and propagation of cracking in the RC columns during compressive loading. The AE characteristics of corroded RC columns are in agreement with the macroscopic failure behaviors observed during the damage and failure process. A damage evolution model of corroded RC columns based on the AE parameters is proposed.

Dataset on carbonation and chloride-induced steel corrosion in cementitious mortars.

Machine learning (ML) has seen success in civil and structural engineering, but its application to forecasting corrosion of steel reinforcement in concrete structures is limited due to small datasets from isolated studies. Moreover, the existing corrosion dataset of reinforced concrete typically lacks sufficient and comprehensive material and environmental information that enables reliable corrosion prediction of reinforced concrete under complex corrosion scenarios. This work aims to bridge the gap by compiling and building a comprehensive corrosion dataset focusing on carbon steel in cementitious mortars. This dataset involves 46 distinct mortar mixtures with embedded steel bars. The samples first underwent accelerated corrosion testing (either by carbonation or chloride contamination), followed by investigating their corrosion behaviours under varying relative humidity (RH) conditions. Corrosion data were obtained during this period, in which all corrosion measurements were conducted in laboratory settings and the results are tabulated in spreadsheet format (.xlsx). The dataset encompasses mixture parameters, material properties, environmental parameters, and electrochemical parameters. This extensive dataset provides valuable corrosion data for training ML models to predict steel corrosion across various corrosion-related variables.

Machine learning-based corrosion rate prediction of steel embedded in soil.

Predicting the corrosion rate for soil-buried steel is significant for assessing the service-life performance of structures in soil environments. However, due to the large amount of variables involved, existing corrosion prediction models have limited accuracy for complex soil environment. The present study employs three machine learning (ML) algorithms, i.e., random forest, support vector regression, and multilayer perception, to predict the corrosion current density of soil-buried steel. Steel specimens were embedded in soil samples collected from different regions of the Wisconsin state. Variables including exposure time, moisture content, pH, electrical resistivity, chloride, sulfate content, and mean total organic carbon were measured through laboratory tests and were used as input variables for the model. The current density of steel was measured through polarization technique, and was employed as the output of the model. Of the various ML algorithms, the random forest (RF) model demonstrates the highest predictability (with an RMSE value of 0.01095 A/m2 and an R2 value of 0.987). In light of the feature selection method, the electrical resistivity is identified as the most significant feature. The combination of three features (resistivity, exposure time, and mean total organic carbon) is the optimal scenario for predicting the corrosion current density of soil-buried steel.

Structural composition of antibacterial zinc-doped geopolymers.

Elucidating the structural composition of a three-dimensional amorphous sodium-aluminosilicate-hydrate (Na2O-Al2O3-SiO2-H2O, N-A-S-H) gel in geopolymers is a prerequisite for its prevailing application in biomaterials, construction, waste management, and climate change mitigation. An unsolved challenge in geopolymer science is the clear structural understanding of amorphous N-A-S-H doped with desired metals. Here, we uncover the molecular structure of (Zn)-N-A-S-H, confirming the tetrahedral coordination of Zn with O and the presence of Si-O-Zn bonds. The Zn-Si distance of ∼3.0-3.1 Å proves the connection of the corners of ZnO42- tetrahedra and SiO4 tetrahedra by slight twisting. The stoichiometric formula of the ZnO-doped geopolymer is quantified as (Na0.19Zn0.02AlSi1.74O5.095)·0.19H2O. The remarkable antimicrobial efficacy of the Zn modified-geopolymer in inhibiting the formation of biofilms by the sulphur-oxidising bacteria Acidithiobacillus thiooxidans and inhibiting biogenic acidification is evidenced. The biodegradation process of the geopolymer featuring the rupture of the Si-O-Al and Si-O-Zn bonds of the networks leads to the expelling of tetrahedral AlO4- and ZnO42- from the aluminosilicate framework and the eventual formation of the siliceous structure. This work demonstrates that the (Zn)-N-A-S-H structure of our new geopolymer provides a solution to optimising geopolymer materials and provides further possibilities for designing novel geopolymer composites for use in construction materials, antibacterial biomaterials in dental or bone surgery, and management of hazardous and radioactive waste.

The opening of mitochondrial permeability transition pore (mPTP) and the inhibition of electron transfer chain (ETC) induce mitophagy in wheat roots under waterlogging stress.

Mitochondria are crucial for the regulation of intracellular energy metabolism, biosynthesis, and cell survival. And studies have demonstrated the role of mitochondria in oxidative stress-induced autophagy in plants. Previous studies found that waterlogging stress can induce the opening of mitochondrial permeability transition pore (mPTP) and the release of cytochrome c in endosperm cells, which proved that mPTP plays an important role in the programmed cell death of endosperm cells under waterlogging stress. This study investigated the effects of the opening of mPTP and the inhibition of ETC on mitophagy in wheat roots under waterlogging stress. The results showed that autophagy related genes in the mitochondria of wheat root cells could respond to waterlogging stress; waterlogging stress led to the degradation of the characteristic proteins cytochrome c and COXII in the mitochondria of root cells. With the prolongation of waterlogging time, the protein degradation degree and the occurrence of mitophagy gradually increased. Under waterlogging stress, exogenous mPTP opening inhibitor CsA inhibited mitophagy in root cells and alleviated mitophagy induced by flooding stress, while exogenous mPTP opening inducer CCCP induced mitophagy in root cells; exogenous mPTP opening inducer CCCP induced mitophagy in root cells. The electron transfer chain inhibitor antimycin A induces mitophagy in wheat root cells and exacerbates mitochondrial degradation. In conclusion, waterlogging stress led to the degradation of mitochondrial characteristic proteins and the occurrence of mitophagy in wheat root cells, and the opening of mPTP and the inhibition of ETC induced the occurrence of mitophagy.

Experimental Investigation and Analytical Modeling of Chloride Diffusivity of Fly Ash Concrete.

Owing to its importance in the assessment of reinforced concrete structures, it is essential to determine the chloride diffusivity of fly ash concrete. This paper presents an investigation into the diffusion characteristics of chloride ions in fly ash concrete. Through experiment, the relationship between chloride diffusivity and curing age up to 1800 days is measured and the effects of curing age, water/binder ratio, aggregate volume fraction, and fly ash content (i.e., percentage of total cementitious material by mass) on chloride diffusivity are evaluated. It is found that the chloride diffusivity decreases with the increase of curing age, aggregate volume fraction, and fly ash content, but increases with the increase of water/binder ratio. In analytical modeling, an equivalent aggregate model is constructed and the equivalent interfacial transition zone (ITZ) thickness is derived analytically. With the equivalent aggregate model, three-phase fly ash concrete reduces to a two-phase composite material. By extending the Maxwell method, the chloride diffusivity of fly ash concrete is formulated. Finally, the validity of the analytical method is verified by experimental results.

Fractal Characterization of Non-Uniform Corrosion of Steel Bars in Concrete Beams after Accelerated depassivation and Seven-Year Natural Corrosion.

In this work, the non-uniform corrosion characteristics of steel bars in stressed reinforced concrete beams after accelerated depassivation and seven-year outdoor natural corrosion is analyzed using fractal theory. 3D laser scanning and 3D reconstruction technology are applied to collect the cross-sectional area along the steel bars and obtain the corrosion curves. The non-uniformity of corrosion is analyzed by fractal dimensions which is calculated by variation method. The results indicate that the initial loading level and loading zone have some influence on non-uniform characteristics of steel bars. For an ordinary concrete beam, the increase of load can cause a reduction of fractal dimension of corrosion curves by 5%, which indicates the non-uniformity of corrosion will increase with the increase of load level. The fractal dimension in the bending zone is lower than that in the tension-shear zone, which indicates that corrosion is more non-uniform in bending zone. However, the loading level and loading zone have a slight influence on corrosion level, and the maximum difference of corrosion level caused by load is merely 0.23%. Furthermore, the corrosion level increases with the decrease of fractal dimension, suggesting that the non-uniformity of corrosion increases with the growth of corrosion level. The incorporation of slag powder can help reduce the non-uniformity of corrosion, but the influence on reduction of the corrosion level is about 0.25%. For concrete structures under marine environment, application of slag powder is a good method to slow down the corrosion and reduce the non-uniformity of corrosion.