Green and effective remediation of heavy metals contaminated water using CaCO3 vaterite synthesized through biomineralization.

Heavy metal pollution has attracted significant attention due to its persistent presence in aquatic environments. A novel vaterite-based calcium carbonate adsorbent, named biogenic CaCO3, was synthesized utilizing a microbially induced carbonate precipitation (MICP) method to remediate heavy metal-contaminated water. The maximum Cd2+ removal capacity of biogenic CaCO3 was 1074.04 mg Cd2+/g CaCO3 with a high Cd2+ removal efficiency greater than 90% (initial Cd2+ concentration 400 mg/L). Furthermore, the biogenic CaCO3 vaterite, induced by microbial-induced calcium carbonate precipitation (MICP) process, demonstrated a prolonged phase transformation to calcite and enhanced stability. This resulted in a sustained high effectiveness (greater than 96%) following six consecutive recycling tests. Additionally, X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses revealed that the semi-stable vaterite type of biogenic CaCO3 spontaneously underwent dissolution and recrystallization to form thermodynamic stable calcite in aquatic environments. However, the presence of Cd2+ leads to the transformation of vaterite into CdCO3 rather than undergoing direct converting to calcite. This transformation is attributed to the relatively low solubility of CdCO3 compared to calcite. Meanwhile, the biogenic CaCO3 proved to be an efficient and viable method for the removal of Pb2+, Cu2+, Zn2+, Co2+, Ni2+ and Mn2+ from water samples, surpassing the performance of previously reported adsorbents. Overall, the efficient and promising adsorbent demonstrates potential for practical in situ remediation of heavy metals-contaminated water.

MetaAc4C: A multi-module deep learning framework for accurate prediction of N4-acetylcytidine sites based on pre-trained bidirectional encoder representation and generative adversarial networks.

MOTIVATION: N4-acetylcytidine (ac4C) is a highly conserved RNA modification that plays a crucial role in various biological processes. Accurately identifying ac4C sites is of paramount importance for gaining a deeper understanding of their regulatory mechanisms. Nevertheless, the existing experimental techniques for ac4C site identification are characterized by limitations in terms of cost-effectiveness, while the performance of current computational methods in accurately identifying ac4C sites requires further enhancement. RESULTS: In this paper, we present MetaAc4C, an advanced deep learning model that leverages pre-trained bidirectional encoder representations from transformers (BERT). The model is based on a bi-directional long short-term memory network (BLSTM) architecture, incorporating attention mechanism and residual connection. To address the issue of data imbalance, we adapt generative adversarial networks to generate synthetic feature samples. On the independent test set, MetaAc4C surpasses the current state-of-the-art ac4C prediction model, exhibiting improvements in terms of ACC, MCC, and AUROC by 2.36%, 4.76%, and 3.11%, respectively, on the unbalanced dataset. When evaluated on the balanced dataset, MetaAc4C achieves improvements in ACC, MCC, and AUROC by 2.6%, 5.11%, and 1.01%, respectively. Notably, our approach of utilizing WGAN-GP augmented training RNA samples demonstrates even superior performance compared to the SMOTE oversampling method.

Investigation of the antifungal activity of the dicarboximide fungicide iprodione against Bipolaris maydis.

Southern corn leaf blight (SCLB), mainly caused by Bipolaris maydis, is a destructive disease of maize worldwide. Iprodione is a widely used dicarboximide fungicide (DCF); however, its antifungal activity against B. maydis has not been well studied until now. In this study, the sensitivity of 103 B. maydis isolates to iprodione was determined, followed by biochemistry and physiology assays to ascertain the fungicide's effect on the morphology and other biological properties of B. maydis. The results indicated that iprodione exhibited strong inhibitory activity against B. maydis, and the EC50 values in inhibiting mycelial growth ranged from 0.088 to 1.712 µg/mL, with a mean value of 0.685 ± 0.687 µg/mL. After treatment with iprodione, conidial production of B. maydis was decreased significantly, and the mycelia branches increased with obvious shrinkage, distortion and fracture. Moreover, the expression levels of the osmotic pressure-related regulation genes histidine kinase (hk) and Ssk2-type mitogen-activated protein kinase (ssk2) were upregulated, the glycerin content of mycelia increased significantly, the relative conductivity of mycelia increased, and the cell wall membrane integrity was destroyed. The in vivo assay showed that iprodione at 200 µg/mL provided 79.16% protective efficacy and 90.92% curative efficacy, suggesting that the curative effect was better than the protective effect. All these results proved that iprodione exhibited strong inhibitory activity against B. maydis and provided excellent efficacy in controlling SCLB, indicating that iprodione could be an alternative candidate for the control of SCLB in China.

A PETAR method for risk assessment of human health and environment on the regional scale.

Risk assessments are necessary to effectively reveal the state of the degradation of living environments on a regional scale. However, risk assessments are often limited by time, cost, and technology, which make conducting effective evaluations difficult. Thus, in this study, the procedure for ecological tiered assessment of risk (PETAR) method was used to analyze the human health and environmental risks in Daye, China. This method first used the United States Environmental Protection Agency's risk assessment approach to qualitatively determine the risk sources, pressures, receptors, and effect endpoints and constructed a conceptual model of threats to the human living environment. Each risk-prone subregion was then evaluated using the fuzzy logic method. Next, a quantitative assessment was conducted for the subregions with the most serious environmental degradation. Finally, quantitative analyses were performed to verify the original hypotheses. The results showed that the high-risk areas were distributed in the industrial regions of Daye, wherein mining and processing clusters and mining settlements are widespread and confirmed the locations of the particular subregions with the most serious human health and environmental risks. This study also validated the practicality of the PETAR method for human health risk assessments in mining areas with large-scale, multifactor, and multihazard paths. Integr Environ Assess Manag 2023;19:239-253. © 2022 SETAC.

Transcriptome expression profiles reveal response mechanisms to drought and drought-stress mitigation mechanisms by exogenous glycine betaine in maize.

Drought stress is one of the major abiotic stresses that limit growth, development and yield of maize crops. To better understand the responses of maize inbred lines with different levels of drought resistance and the molecular mechanism of exogenous glycine betaine (GB) in alleviating drought stress, the responses of two maize inbred lines to drought stress and to the stress-mitigating effects of exogenous GB were investigated. Seedling morphology, physiological and biochemical indexes, root cell morphology and root transcriptome expression profiles were compared between a drought-tolerant inbred line Chang 7-2 and drought-sensitive inbred line TS141. Plants of both lines were subjected to treatments of drought stress alone and drought stress with application of exogenous GB. The results showed that with the increase of drought treatment time, the growth and development of TS141 were inhibited, while those of Chang 7-2 were not significantly different from those of the control (no drought stress and GB). Compared with the corresponding data of the drought-stress group, every index measured from the two inbred lines indicated mitigating effects from exogenous GB, and TS141 produced stronger mitigating responses due to the GB. Transcriptome analysis showed that 562 differentially expressed genes (DEGs) were up-regulated and 824 DEGs were down-regulated in both inbred lines under drought stress. Due to the exogenous GB, 1061 DEGs were up-regulated and 424 DEGs were down-regulated in both lines. In addition, quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify 10 DEGs screened from the different treatments. These results showed that the expression of 9 DEGs were basically consistent with their respective transcriptome expression profiles. The results of this study provide models of potential mechanisms of drought tolerance in maize as well as potential mechanisms of how exogenous GB may regulate drought tolerance.

Study on ZmRPN10 Regulating Leaf Angle in Maize by RNA-Seq.

Ubiquitin/proteasome-mediated proteolysis (UPP) plays a crucial role in almost all aspects of plant growth and development, proteasome subunit RPN10 mediates ubiquitination substrate recognition in the UPP process. The recognition pathway of ubiquitinated UPP substrate is different in different species, which indicates that the mechanism and function of RPN10 are different in different species. However, the homologous ZmRPN10 in maize has not been studied. In this study, the changing of leaf angle and gene expression in leaves in maize wild-type B73 and mutant rpn10 under exogenous brassinosteroids (BRs) were investigated. The regulation effect of BR on the leaf angle of rpn10 was significantly stronger than that of B73. Transcriptome analysis showed that among the differentially expressed genes, CRE1, A-ARR and SnRK2 were significantly up-regulated, and PP2C, BRI1 AUX/IAA, JAZ and MYC2 were significantly down-regulated. This study revealed the regulation mechanism of ZmRPN10 on maize leaf angle and provided a promising gene resource for maize breeding.