Incorporation of chemical and toxicological availability into metal mixture toxicity modeling: State of the art and future perspectives

In the real world, metals are generally present as mixtures, but evaluating their mixture toxicity is still a daunting challenge. The classic conceptual models of concentration addition (CA) and independent action (IA) have been widely used by simply adding doses and responses to predict mixture effects assuming there is non-interaction. In cases where interactions do occur in a mixture, both CA and IA are no longer applicable for quantifying the toxicity, because interpretation of the observed joint effects is often limited to overall antagonism or synergism. In metal mixtures, interactive effects may occur at various levels, such as the exposure level, the uptake level, and the target level. A comprehensive understanding of the mechanisms of joint toxicity is therefore needed to incorporate the interactive effects of mixture components in predicting mixture toxicity. With this in mind, numerous bioavailability-based methods may be considered, with diverse mechanistic perspectives, such as the biotic ligand model (BLM), the electrostatic toxicity model (ETM), the WHAM-F tox approach, a toxicokinetic-toxicodynamic (TK-TD) and an omics-based approach. This review therefore timely summarizes the representative predictive tools and their underlying mechanisms and highlights the importance of integrating mixture interactions and bioavailability in assessing the toxicity and risks of metal mixtures

Optimization of SM4 Encryption Algorithm for Power Metering Data Transmission

This study focuses on enhancing the security of the SM4 encryption algorithm for power metering data transmission by employing hybrid algorithms to optimize its substitution box (S-box). A multi-objective fitness function is constructed to evaluate the S-box structure, aiming to identify design solutions that satisfy differential probability, linear probability, and non-linearity balance. To achieve global optimization and local search for the S-box, a hybrid algorithm model that combines genetic algorithm and simulated annealing is introduced. This approach yields significant improvements in optimization effects and increased non-linearity. Experimental results demonstrate that the optimized S-box significantly reduces differential probability and linear probability while increasing non-linearity to 112. Furthermore, a comparison of the ciphertext entropy demonstrates enhanced encryption security with the optimized S-box. This research provides an effective method for improving the performance of the SM4 encryption algorithm

Model-based rationalization of mixture toxicity and accumulation in Triticum aestivum upon concurrent exposure to yttrium, lanthanum, and cerium

Rare earth elements (REEs) often co-exist in the environment, but predicting their ‘cocktail effects’ is still challenging, especially for high-order mixtures with more than two components. Here, we systematically investigated the toxicity and accumulation of yttrium, lanthanum, and cerium mixtures in Triticum aestivum following a standardized bioassay. Toxic effects of mixtures were predicted using the reference model of Concentration Addition (CA), Ternary model, and Ternary-Plus model. Interactions between the REEs in binary and ternary mixtures were determined based on external and internal concentrations, and their magnitude estimated from the parameters deviated from CA. Strong antagonistic interactions were found in the ternary mixtures even though there were no significant interactions in the binary mixtures. Predictive ability increased when using the CA model, Ternary model, and Ternary-Plus model, with R2 = 0.78, 0.80, and 0.87 based on external exposure concentrations, and R2 = 0.72, 0.73, and 0.79, respectively based on internal concentrations. The bioavailability-based model WHAM-FTOX explained more than 88 % and 85 % of the toxicity of binary and ternary REE treatments, respectively. Our result showed that the Ternary-Plus model and WHAM-FTOX model are promising tools to account for the interaction of REEs in mixtures and could be used for their risk assessment

Interactions of arsenic, copper, and zinc in soil-plant system:Partition, uptake and phytotoxicity

Arsenic, copper, and zinc are common elements found in contaminated soils but little is known about their combined effects on plants when presented simultaneously. Here, we systematically investigated the phytotoxicity and uptake of binary and ternary mixtures of As, Cu, and Zn in a soil-plant system, using wheat (Triticum aestivum) as model species. The reference models of concentration addition (CA) and response addition (RA) coupled with different expressions of exposure (total concentrations in soil ([M]tot, mg/kg), free ion activities in soil solution ({M}, μM), and internal concentrations in plant roots ([M]int, μg/g)), were selected to assess the interaction mechanisms of binary mixtures of As–Cu, As–Zn, and Cu–Zn. Metal(loid) interactions in soil were estimated in terms of solution-solid partitioning, root uptake, and root elongation effects. The partitioning of one metal(loid) between the soil solution and solid phase was most often inhibited by the presence of the other metal(loid). In terms of uptake, inhibitory effects and no effects were observed in the mixtures of As, Cu, and Zn, depending on the mixture combinations and the dose metrics used. In terms of toxicity, simple (antagonistic or synergistic) and more complex (dose ratio-dependent or dose level-dependent) interaction patterns of binary mixtures occurred, depending on the dose metrics selected and the reference models used. For ternary mixtures (As-Cu-Zn), nearly additive effects were observed irrespective of dose descriptors and reference models. The observed interactions in this study may help to understand and predict the joint toxicity of metal(loid)s mixtures in soil-plant system. Mixture interactions and bioavailability should be incorporated into the regulatory framework for accurate risk assessment of multimetal-contaminated sites

Optimization of SM4 Encryption Algorithm for Power Metering Data Transmission

This study focuses on enhancing the security of the SM4 encryption algorithm for power metering data transmission by employing hybrid algorithms to optimize its substitution box (S-box). A multi-objective fitness function is constructed to evaluate the S-box structure, aiming to identify design solutions that satisfy differential probability, linear probability, and non-linearity balance. To achieve global optimization and local search for the S-box, a hybrid algorithm model that combines genetic algorithm and simulated annealing is introduced. This approach yields significant improvements in optimization effects and increased non-linearity. Experimental results demonstrate that the optimized S-box significantly reduces differential probability and linear probability while increasing non-linearity to 112. Furthermore, a comparison of the ciphertext entropy demonstrates enhanced encryption security with the optimized S-box. This research provides an effective method for improving the performance of the SM4 encryption algorithm

A transcript profiling approach reveals the zinc finger transcription factor ZNF191 is a pleiotropic factor

BACKGROUND: The human zinc finger protein 191 (ZNF191) is a member of the SCAN domain family of Krüppel-like zinc finger transcription factors. ZNF191 shows 94% identity to its mouse homologue zinc finger protein 191(Zfp191), which is the most highly conserved among the human-mouse SCAN family member orthologues pairs. Zfp191 is widely expressed during early embryogenesis and in adult organs. Moreover, Zfp191(-/- )embryos have been shown to be severely retarded in development and die approximately at embryonic day E7.5. ZNF191 can specifically interact with the widespread TCAT motif which constitutes the HUMTH01 microsatellite in the tyrosine hydroxylase (TH) gene. Allelic variations of HUMTH01 have been stated to have a quantitative silencing effect on TH gene expression and to correlate with quantitative and qualitative changes in the binding by ZNF191. In addition, ZNF191 displays a suppressive effect on the transcription; however, little downstream targets have identified. RESULTS: We searched for ZNF191 target genes by using a transient overexpression and knockdown strategy in the human embryo kidney (HEK293) cells. Microarray analyses identified 6094 genes modulated by overexpression of ZNF191 and 3332 genes regulated by knockdown of ZNF191, using a threshold of 1.2-fold. Several interested candidate genes, validated by real time RT-PCR, were correlated well with the array data. Interestingly, 1456 genes were identified in both transient overexpression and transient knockdown strategies. The GenMAPP and MappFinder software packages were further used for pathway analysis of these significantly altered genes. Several gene pathways were found to be involved in processes of the regulation of kinase activity, transcription, angiogenesis, brain development and response to DNA damage. CONCLUSION: Our analysis reveals for the first time that ZNF191 is a pleiotropic factor that has a role in hematopoiesis, brain development and cancers

A String-Inspired Quintom Model Of Dark Energy

We propose in this paper a quintom model of dark energy with a single scalar field $\phi$ given by the lagrangian ${\cal L}=-V(\phi)\sqrt{1-\alpha^\prime\nabla_{\mu}\phi\nabla^{\mu}\phi +\beta^\prime \phi\Box\phi}$. In the limit of $\beta^\prime\to$0 our model reduces to the effective low energy lagrangian of tachyon considered in the literature. We study the cosmological evolution of this model, and show explicitly the behaviors of the equation of state crossing the cosmological constant boundary.Comment: 6 pages, 4 figures, accepted by PL

Gaussian Boson Sampling with Pseudo-Photon-Number Resolving Detectors and Quantum Computational Advantage

We report new Gaussian boson sampling experiments with pseudo-photon-number-resolving detection, which register up to 255 photon-click events. We consider partial photon distinguishability and develop a more complete model for characterization of the noisy Gaussian boson sampling. In the quantum computational advantage regime, we use Bayesian tests and correlation function analysis to validate the samples against all current classical mockups. Estimating with the best classical algorithms to date, generating a single ideal sample from the same distribution on the supercomputer Frontier would take ~ 600 years using exact methods, whereas our quantum computer, Jiuzhang 3.0, takes only 1.27 us to produce a sample. Generating the hardest sample from the experiment using an exact algorithm would take Frontier ~ 3.1*10^10 years.Comment: submitted on 10 Apri