Enhancing subsurface contamination assessment via ensemble prediction of ground electrical property: A Colorado AMD-impacted wetland case study.

Acid mine drainage (AMD) is recognized as a major environmental challenge in the Western United States, particularly in Colorado, leading to extreme subsurface contamination issue. Given Colorado's arid climate and dependence on groundwater, an accurate assessment of AMD-induced contamination is deemed crucial. While in past, machine learning (ML)-based inversion algorithms were used to reconstruct ground electrical properties (GEP) such as relative dielectric permittivity (RDP) from ground penetrating radar (GPR) data for contamination assessment, their inherent non-linear nature can introduce significant uncertainty and non-uniqueness into the reconstructed models. This is a challenge that traditional ML methods are not explicitly designed to address. In this study, a probabilistic hybrid technique has been introduced that combines the DeepLabv3+ architecture-based deep convolutional neural network (DCNN) with an ensemble prediction-based Monte Carlo (MC) dropout method. Different MC dropout rates (1%, 5%, and 10%) were initially evaluated using 1D and 2D synthetic GPR data for accurate and reliable RDP model prediction. The optimal rate was chosen based on minimal prediction uncertainty and the closest alignment of the mean or median model with the true RDP model. Notably, with the optimal MC dropout rate, prediction accuracy of over 95% for the 1D and 2D cases was achieved. Motivated by these results, the hybrid technique was applied to field GPR data collected over an AMD-impacted wetland near Silverton, Colorado. The field results underscored the hybrid technique's ability to predict an accurate subsurface RDP distribution for estimating the spatial extent of AMD-induced contamination. Notably, this technique not only provides a precise assessment of subsurface contamination but also ensures consistent interpretations of subsurface condition by different environmentalists examining the same GPR data. In conclusion, the hybrid technique presents a promising avenue for future environmental studies in regions affected by AMD or other contaminants that alter the natural distribution of GEP.

Nature-inspired stochastic hybrid technique for joint and individual inversion of DC and MT data.

Here, a new naturally-inspired stochastic nonlinear joint and individual inversion technique for integrating direct current (DC) and magnetotelluric (MT) data interpretation-based simulation of a swarm intelligence combo with specific capabilities for exploitation of the variable weight particle swarm optimizer (vPSO) and exploration of the grey wolf optimizer (GWO), vPSOGWO, is used. They are particularly notable for their capacity for information exchange while hunting for food. Through synthetic MT and DC data contaminated with various sets of random noise, the applicability of the anticipated vPSOGWO algorithm based joint and individual inversion algorithm was assessed. The field examples, collected from diversified different geological terrains, including Digha (West Bengal), India; Sundar Pahari (Jharkhand), India; Puga Valley (Ladakh), India; New Brunswick, Canada; and South Central Australia, have shown the practical application of the proposed algorithm. Further, a Bayesian probability density function (bpdf) for estimating a mean global model and uncertainty assessment in posterior; and a histogram for model resolution assessment have also been created using 1000 inverted models. We examined the inverted outcomes and compared them with results from other cutting-edge methodologies, including the GWO, PSO, genetic algorithm (GA), Levenberg-Marquardt (LM), and ridge-regression (RR). Our findings showed that the current methodology is more effective than the GWO, PSO, GA, LM, and RR techniques at consistently improving the convergence of the global minimum. In contrast to earlier approaches, the current cutting-edge strategy vPSOGWO offers an improved resolution of an additional significant crustal thickness of about 65.68 ± 1.96 km over the Puga Valley, in which the inverted crustal thickness determined by vPSOGWO agrees well with the published crustal thickness over the Puga Valley. The new technology brings simulations closer to genuine models by significantly reducing uncertainty and enhancing model resolution.

Dusquetide modulates innate immune response through binding to p62.

SQSTM1/p62 is an autophagic receptor that plays a major role in mediating stress and innate immune responses. Preclinical studies identified p62 as a target of the prototype innate defense regulator (IDR); however, the molecular mechanism of this process remains unclear. Here, we describe the structural basis and biological consequences of the interaction of p62 with the next generation of IDRs, dusquetide. Both electrostatic and hydrophobic contacts drive the formation of the complex between dusquetide and the ZZ domain of p62. We show that dusquetide penetrates the cell membrane and associates with p62 in vivo. Dusquetide binding modulates the p62-RIP1 complex, increases p38 phosphorylation, and enhances CEBP/B expression without activating autophagy. Our findings provide molecular details underlying the IDR action that may help in the development of new strategies to pharmacologically target p62.

Protocol to identify drug-binding sites in proteins using solution NMR spectroscopy.

Dusquetide is a next-generation IDR (innate defense regulator) targeting the major autophagy receptor protein SQSTM1/p62 and modulating the innate immune response. Here, we describe a protocol for determining dusquetide-binding sites of p62 by solution NMR spectroscopy. Step-by-step technique details were provided, including sample preparation, NMR experiment setup, data processing, and binding site analysis. This protocol could be applied to characterize other small molecules targeting the ZZ domain of p62 (9 kDa) or other proteins containing ZZ domains. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2022).