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Three-dimensional inversion of magnetic data can obtain the distribution of subsurface magnetic targets. Deep learning is an effective way to achieve 3-D inversion, which trains a neural network to learn the features of magnetic anomaly data and then generates a 3-D model based on these features. Large training samples are required to achieve persuasive results due to the limited observational data and the multisolution nature of the inverse problem. To reduce the nonuniqueness of the inversion, this article proposes a multivariate magnetic data-based deep learning 3-D inversion strategy. With the proposed strategy, more domain knowledge is incorporated into the training data of the neural network to improve the inversion accuracy. The input data of the neural network adopt multivariate observation data, including multiscale data and multitype data such as magnetic three-component data, magnetic gradient tensor data, and so on, and output a 3-D model to realize 3-D to 3-D mapping. Then, the neural network structure uses the 3-D convolution to extract 3-D spatial information. Both tests on simulation and measured data verify that the proposed strategy can effectively improve the accuracy of the 3-D magnetic inversion.

This study aimed to investigate whether the beneficial effects of PCA on chondrocyte senescence are mediated through the regulation of mitophagy. Chondrocyte senescence plays a significant role in the development and progression of knee osteoarthritis (OA). The compound protocatechuic aldehyde (PCA), which is abundant in the roots of Salvia miltiorrhiza, has been reported to have antioxidant properties and the ability to protect against cellular senescence. To achieve this goal, a destabilization of the medial meniscus (DMM)-induced mouse OA model and a lipopolysaccharide (LPS)-induced chondrocyte senescence model were used, in combination with PINK1 gene knockdown or overexpression. After treatment with PCA, cellular senescence was assessed using Senescence-Associated β-Galactosidase (SA-β-Gal) staining, DNA damage was evaluated using Hosphorylation of the Ser-139 (γH2AX) staining, reactive oxygen species (ROS) levels were measured using Dichlorodihydrofluorescein diacetate (DCFH-DA) staining, mitochondrial membrane potential was determined using a 5,5',6,6'-TETRACHLORO-1,1',3,3'-*. TETRAETHYBENZIMIDA (JC-1) kit, and mitochondrial autophagy was examined using Mitophagy staining. Western blot analysis was also performed to detect changes in senescence-related proteins, PINK1/Parkin pathway proteins, and mitophagy-related proteins. Our results demonstrated that PCA effectively reduced chondrocyte senescence, increased the mitochondrial membrane potential, facilitated mitochondrial autophagy, and upregulated the PINK1/Parkin pathway. Furthermore, silencing PINK1 weakened the protective effects of PCA, whereas PINK1 overexpression enhanced the effects of PCA on LPS-induced chondrocytes. PCA attenuates chondrocyte senescence by regulating PINK1/Parkin-mediated mitochondrial autophagy, ultimately reducing cartilage degeneration.

The induced polarization (IP) method holds a strong potential to better characterize the critical zone of our planet especially in areas characterized by multi‐phase flow. Power‐law relationships between the bulk, surface, and quadrature conductivities versus the pore water saturation are potentially useable to map the subsurface water content distribution. However, the saturation exponents nand pin these power‐law relationships have been observed to vary with the texture of geomaterials and the wettabilities of pore fluids. Traditional experimental setups in the laboratory do not allow to independently visualize the pore fluid distribution. Therefore, the physical interpretations of the two saturation exponents have remained unclear. We developed a novel milli‐fluidic micromodel using clay‐coated glass beads that exhibit excellent visibility and high IP response. Through laboratory experiments, we simultaneously determined the micromodel complex conductivity and acquired the corresponding pore‐scale fluid distributions generated by drainage and imbibition through such class of porous materials. Finite‐element simulations of complex conductivity based on the upscaling of the complex surface conductance of grains were conducted to determine the saturation exponents under ideal pore fluid distributions. Results indicate that saturation exponents nand pvary depending on the ganglia size of the insulating fluids. The saturation exponents nand pexhibit power‐law relationships with the change rate of pore water connectivity with saturation, which is calculated through the computation of the derivative of Euler characteristics. These findings provide a new physical explanation to the relationships between the saturation exponents and the microscopic fluid distributions within the geomaterials. Water saturation of porous bodies can be related to the complex conductivity through power‐law relationships. The existence of these power‐law relationships has been clearly documented in the literature. They are critical in the realm of hydrogeophysics to better characterize the critical zone of the solid Earth. However, the values of the saturation exponents in these power‐laws have been observed to vary with different material textures and no underlying mechanisms have been able to explain these variations to date. This lack of physical understanding could limit the applicability of the induced polarization method to characterize the critical zone especially when immiscible fluid phases are present. To tackle this, we developed a milli‐fluidic pore model that allows investigating the saturation exponents while monitoring the pore‐scale fluid distributions. Together with numerical simulations for ideal fluid distribution cases, we found a relationship between the saturation exponents and a microscopic pore parameter called “change rate of the pore water connectivity with saturation.” These findings suggest that when estimating subsurface water saturation from electrical parameters, taking the pore fluid distribution into account can significantly improve the estimation accuracy, therefore enhancing the efficiency of geo‐electrical applications for a better characterization of hydrocarbon contaminated aquifers and oil reservoirs. A micromodel setup allows for simultaneous pore fluid visualization and complex electrical conductivity measurementThe two saturation exponents for the in‐phase and quadrature conductivities are correlated with the ganglia size of the insulating phaseA power‐law‐type relationship between the saturation exponents and the change rate of water connectivity with saturation is demonstrated A micromodel setup allows for simultaneous pore fluid visualization and complex electrical conductivity measurement The two saturation exponents for the in‐phase and quadrature conductivities are correlated with the ganglia size of the insulating phase A power‐law‐type relationship between the saturation exponents and the change rate of water connectivity with saturation is demonstrated

The mass discharge emanating from dense non‐aqueous phase liquid (DNAPL) source zones (SZs) is often used as a key metric for risk assessment. To predict the temporal evolution of mass discharge, upscaled models have been developed to approximate the relationship between the depletion of SZ and the mass discharge. A significant challenge stems from the choice of the SZ parameterization, so that a limited number of domain‐averaged SZ metrics can suffice as an input and accurately predict the complex mass‐discharge behavior. Moreover, existing deterministic upscaled models cannot quantify prediction uncertainty stemming from modeling parameterization. To address these challenges, we propose a method based on a Bayesian Neural Network (BNN) which learns the non‐linear relationship between SZ metrics and mass discharge from multiphase‐modeling training data. The proposed BNN‐based upscaled model allows uncertainty quantification since it treats trainable parameters as distributions, and does not require a manual parameterization of the SZ a‐priori. Instead, the BNN model chooses three physically meaningful SZ quantities related to mass discharge as input features. Then, we use the expected gradients method to identify the feature importance for mass‐discharge prediction. We evaluated the proposed model on laboratory‐scale DNAPL dissolution experiments. The results show that the BNN model accurately reproduces the multistage mass‐discharge profiles with fewer parameters than existing upscaled models. Feature importance analysis shows that all chosen features are important and sufficient to reproduce complex mass discharge. This model provides accurate mass‐discharge predictions and uncertainty estimation, therefore holds a great potential for probabilistic risk assessments and decision‐making. We demonstrate a predictive Bayesian Neural Network (BNN) upscaled model for contaminant mass discharge from heterogeneous dense non‐aqueous phase liquid (DNAPL) source zones (SZs)The proposed model accurately captures complex multistage discharge and provides uncertainty bounds for its predictionsThe results highlight the importance of a minimum set of SZ features and parameterization required for accurate mass discharge prediction We demonstrate a predictive Bayesian Neural Network (BNN) upscaled model for contaminant mass discharge from heterogeneous dense non‐aqueous phase liquid (DNAPL) source zones (SZs) The proposed model accurately captures complex multistage discharge and provides uncertainty bounds for its predictions The results highlight the importance of a minimum set of SZ features and parameterization required for accurate mass discharge prediction

ABSTRACTWe aimed to clarify the therapeutic effects of imperatorin (IMP) on knee osteoarthritis (KOA). Thirty 3-month-old SD male rats were randomly divided into Normal, monosodium iodoacetate (MIA) and MIA+IMP groups. Their synovial tissues were subjected to histopathological analysis. Primary synovial fibroblasts obtained from additional normal rats were treated by lipopolysaccharide (LPS) and then IMP. The mRNA and protein expressions of factors related to synovitis and synovial fibrosis were detected by qRT-PCR and Western blotting, respectively. The concentrations of inflammatory factors IL-1β and IL-18 were measured by ELISA. IMP reduced HIF-1α, NLR family pyrin domain-containing 3 inflammasome expression and IL-1β, IL-18 production in synovial fibroblasts induced by LPS. IMP also downregulated synovial fibrosis markers. In vitrostudy revealed that MIA-induced synovitis and synovial fibrosis were relieved by IMP. IMP relieves the inflammation associated with synovitis and synovial fibrosis. It reduces the production of pro-inflammatory mediators and cytokines and inhibits TGF-β1, TIMP-1 and VEGF expressions that promote synovial fibrosis.

The purpose of this study was to evaluate the CT features of hepatic tuberculosis (TB).

Objectives. Knee osteoarthritis (KOA) pain is caused by nociceptors, which are actually sensory nerve fiber endings that can detect stimuli to produce and transmit pain signals, and high levels of NGF in synovial tissue led to peripheral hyperalgesia in KOA. The purpose of this study is to investigate how sensory nerve fibers respond to the NGF/TrKA signal pathway and mediate the peripheral hyperalgesia in KOA rats. Methods. Forty SD male rats were randomly divided into 4 groups: normal, KOA, KOA + NGF, and KOA + siRNA TrKA. KOA model rats were induced by anterior cruciate ligament transection (ACLT). Mechanical and cold withdrawal thresholds (MWT and CWT) were measured 4 times in each group. The synovial tissues were harvested on day 28, and the expressions of NGF, TrKA, TRPV1, IL-1β, and PGP9.5 were determined using western blot, qPCR, and immunofluorescence staining. The primary rat fibroblast-like synoviocytes (FLSs) and DRG cells were divided into 4 groups as in vivo. The expressions of NGF, TrKA, TRPV1, and CGRP in vitro were determined using western blot and qPCR. Results. KOA and intra-articular injection with NGF protein increased both mRNA and protein levels, not only TRPV1, PGP 9.5, and IL-1β in the synovial tissue, but also TRPV1, PGP 9.5, and S100 in the DRG tissue, while above changes were partly reversed after siRNA TrKA intervention. Besides, siRNA TrKA could improve peripheral hyperalgesia and decreased the TRPV1 positive nerve fiber innervation in synovial tissue. The results in vitro were consistent with those in vivo. Conclusion. This study showed the activation of the NGF/TrKA signaling pathway in KOA promoted the release of pain mediators, increased the innervation of sensory nerve fibers in the synovium, and worsened peripheral hyperalgesia. It also showed increased TRPV1 positive sensory innervation in KOA was mediated by NGF/TrKA signaling and exacerbated peripheral hyperalgesia.

Identification of a groundwater contaminant source simultaneously with the hydraulic conductivity in highly heterogeneous media often results in a high‐dimensional inverse problem. In this study, a deep autoregressive neural network‐based surrogate method is developed for the forward model to allow us to solve efficiently such high‐dimensional inverse problems. The surrogate is trained using limited evaluations of the forward model. Since the relationship between the time‐varying inputs and outputs of the forward transport model is complex, we propose an autoregressive strategy, which treats the output at the previous time step as input to the network for predicting the output at the current time step. We employ a dense convolutional encoder‐decoder network architecture in which the high‐dimensional input and output fields of the model are treated as images to leverage the robust capability of convolutional networks in image‐like data processing. An iterative local updating ensemble smoother algorithm is used as the inversion framework. The proposed method is evaluated using a synthetic contaminant source identification problem with 686 uncertain input parameters. Results indicate that, with relatively limited training data, the deep autoregressive neural network consisting of 27 convolutional layers is capable of providing an accurate approximation for the high‐dimensional model input‐output relationship. The autoregressive strategy substantially improves the network's accuracy and computational efficiency. The application of the surrogate‐based iterative local updating ensemble smoother in solving the inverse problem shows that it can achieve accurate inversion results and predictive uncertainty estimates. High‐dimensional inverse problems are often computationally expensive since a large number of forward model evaluations are usually required. A computationally efficient alternative is to replace in the inversion process the forward model with an accurate but fast‐to‐evaluate surrogate model. However, most existing surrogate methods suffer from the “curse of dimensionality.” In this paper, we develop a deep autoregressive neural network‐based surrogate method to efficiently solve such high‐dimensional inverse problems. The autoregressive neural network can efficiently obtain accurate approximations for the time‐dependent outputs of forward models with time‐varying inputs. In addition, a dense convolutional network architecture is employed to transform the surrogate modeling task to an image‐to‐image regression problem by leveraging the robust capability of convolutional networks in image‐like data processing. The curse of dimensionality was tackled though a series of inherent nonlinear projections of the input into low‐dimensional latent spaces. It is shown that the proposed network provides an accurate surrogate of a transport system with high‐dimensional uncertain inputs using relatively limited training data. Reliable inversion results are then obtained with a rather minor computational cost by using the surrogate to replace the forward model in the inversion process. A deep neural network surrogate approach is developed for groundwater contaminant transport in highly heterogeneous mediaThe time‐varying process is captured using an autoregressive modelA 686‐dimensional inverse problem of contaminant source identification in heterogeneous media is addressed

Surrogate strategies are used widely for uncertainty quantification of groundwater models in order to improve computational efficiency. However, their application to dynamic multiphase flow problems is hindered by the curse of dimensionality, the saturation discontinuity due to capillarity effects, and the time dependence of the multi‐output responses. In this paper, we propose a deep convolutional encoder‐decoder neural network methodology to tackle these issues. The surrogate modeling task is transformed to an image‐to‐image regression strategy. This approach extracts high‐level coarse features from the high‐dimensional input permeability images using an encoder and then refines the coarse features to provide the output pressure/saturation images through a decoder. A training strategy combining a regression loss and a segmentation loss is proposed in order to better approximate the discontinuous saturation field. To characterize the high‐dimensional time‐dependent outputs of the dynamic system, time is treated as an additional input to the network that is trained using pairs of input realizations and of the corresponding system outputs at a limited number of time instances. The proposed method is evaluated using a geological carbon storage process‐based multiphase flow model with a 2,500‐dimensional stochastic permeability field. With a relatively small number of training data, the surrogate model is capable of accurately characterizing the spatiotemporal evolution of the pressure and discontinuous CO2saturation fields and can be used efficiently to compute the statistics of the system responses. A surrogate model for uncertainty quantification of dynamic multiphase flows is developed using deep convolutional encoder‐decoder networksA two‐stage network training strategy is proposed to improve the approximation of saturation discontinuityThe method can accurately predict the time‐dependent output fields of a multiphase flow model with a 2,500‐dimensional permeability field

The extraction of core phases through seismic interferometry is important for exploring the seismic structure of the Earth's inner core because it can provide an unprecedented data coverage. However, low signal‐to‐noise ratios and uncertainties in phase arrivals hinder the application of seismic interferometry. With three arrays from the China Regional Seismic Network, South America, and the western United States, we simulate coda interferometry using the one‐dimensional synthetic coda of large earthquakes (magnitude ≥ 7.0) from 2005 to 2012. Compared with the cross correlations of real coda (10,000–40,000 s after earthquakes), the similarities among the simulated waveforms of the core phases (PKIKP, PKIIKP, PKPab, PKIKP2, and PKIIKP2) indicate that reverberations at first‐order discontinuities constitute the major source for coda interferometry. Relative to synthesized Green's functions, the core phases derived from coda interferometry provide reliable phase information but varying amplitudes. In particular, the slownesses are generally consistent with the theoretical calculations, thereby confirming the extracted phases. The simulations prefer the 15‐ to 50‐s period band and indicate that the earthquake distribution has an insignificant effect on the retrieval of core phases at small distances (0–8°). Coda interferometry is scarcely affected by white noise, but it is influenced by the attenuation of the inner core. Experiments on focal mechanisms further suggest that coda signals from dip‐slip earthquakes mainly contribute to the reconstruction of the core phases (except for the simulated PKPab). Core phases are extracted and confirmed through simulations of coda interferometryReverberations at first‐order discontinuities from large earthquakes are the major source to reconstruct the core phasesThe retrieval of core phases is dependent on frequency band and focal mechanism but not significantly affected by source heterogeneities

Global sensitivity analysis (GSA) and uncertainty quantification (UQ) for groundwater modeling are challenging because of the model complexity and significant computational requirements. To reduce the massive computational cost, a cheap‐to‐evaluate surrogate model is usually constructed to approximate and replace the expensive groundwater models in the GSA and UQ. Constructing an accurate surrogate requires actual model simulations on a number of parameter samples. Thus, a robust experimental design strategy is desired to locate informative samples so as to reduce the computational cost in surrogate construction and consequently to improve the efficiency in the GSA and UQ. In this study, we develop a Taylor expansion‐based adaptive design (TEAD) that aims to build an accurate global surrogate model with a small training sample size. TEAD defines a novel hybrid score function to search informative samples, and a robust stopping criterion to terminate the sample search that guarantees the resulted approximation errors satisfy the desired accuracy. The good performance of TEAD in building global surrogate models is demonstrated in seven analytical functions with different dimensionality and complexity in comparison to two widely used experimental design methods. The application of the TEAD‐based surrogate method in two groundwater models shows that the TEAD design can effectively improve the computational efficiency of GSA and UQ for groundwater modeling. We develop an adaptive design strategy for efficient construction of global surrogate modelsThe proposed method can produce an accurate surrogate model with a few parameter samplesThe proposed method shows good performance in sensitivity analysis and uncertainty quantification of groundwater models

To analyze the computed tomography (CT) findings and the reasons for misdiagnosis of female pelvic tuberculosis.

Characterizing residual non‐aqueous phase liquids (NAPLs) in porous media is essential for designing contaminated site remediation strategies. The direct current resistivity method is increasingly being used at sites contaminated by NAPLs, which uses the Archie's second law to connect NAPL saturation with the resistivity of porous media. A power‐law relationship connecting the phase morphology measured by Euler characteristic with resistivity has also been observed at the pore‐scale. Because these relationships are limited for porous media with specific wettability, previous research works have demonstrated deviations from Archie's second law in both the laboratory and field experiments. To evaluate the effects of residual NAPL morphology with different wettability on bulk resistivity, we developed two 2D‐micromodels and measured their electrical resistivity and phase morphology during phase displacement under laboratory‐controlled conditions. Furthermore, pore network simulations of a variety of conditions were performed in absence of surface conduction. Results show that NAPL distributed in dead ends of the pore space has limited influence on bulk resistivity since they do not control the current path and voltage distribution. The correlations between resistivity index and Euler characteristic are determined by NAPL ganglia size and pore throat width. A power‐law relationship is found between the Archie's saturation exponent and the average size of the residual NAPL ganglia, indicating that this exponent can be interpreted as the rate of change of the pore water connectivity with saturation. These insights provide a physical framework connecting the morphology and wettability of the NAPL ganglia with the Archie's saturation exponent. Petrophysical relationships can relate the non‐aqueous phase liquids (NAPLs) saturation with subsurface resistivity, which help us to characterize the underground contaminants. However, researchers often encountered deviations from widely adopted petrophysical relationships that link the electrical resistivity with NAPL saturation because they neglect or wrongly predict the effect of NAPL morphology on resistivity. In our study, we systematically explored the mechanism behind the impact of NAPL morphology on electrical resistivity by micromodel experiments and pore network simulation. Non‐aqueous phase liquid (NAPL) trapped in dead ends of porous space has little effect on bulk resistivityRelationship between the bulk resistivity and Euler characteristic of partially saturated pore network is determined by NAPL wettabilityA power‐law relationship is found between the Archie's saturation exponent and NAPL ganglia size Non‐aqueous phase liquid (NAPL) trapped in dead ends of porous space has little effect on bulk resistivity Relationship between the bulk resistivity and Euler characteristic of partially saturated pore network is determined by NAPL wettability A power‐law relationship is found between the Archie's saturation exponent and NAPL ganglia size

The challenges posed by high‐resolution characterization of dense nonaqueous phase liquid (DNAPL) source zone architecture (SZA) have motivated the development of simpler upscaled models that rely on domain‐averaged metrics to capture the average mass discharge downstream of source zones (SZ). However, SZA is highly irregular, making estimation of these domain‐averaged metrics from sparse borehole data extremely difficult. Poor estimation of SZ metrics means that upscaled models cannot reproduce the multistage effluent concentrations. Bayesian inversion methods can be used to obtain accurate estimates of SZ metrics and their uncertainties from sparse data, and from there, upscaled models can better reproduce multistage effluent concentrations. This work presents a framework for integrating a deep‐learning‐based 3D SZA inversion method named Convolutional Variational AutoEncoder—Ensemble Smoother with Multiple Data Assimilation (CVAE‐ESMDA) with a process‐based (PB) upscaled mass‐transfer model. This framework can utilize sparse SZ data to estimate directly mass discharge without multiphase modeling. First, CVAE‐ESMDA estimates the SZA by conditioning on sparse data, which is then used as input in the upscaled model for mass‐discharge estimation. We evaluated our framework on two real and 30 synthetic bench‐scale experiments, with significantly different SZAs and multistage effluent concentrations. The results demonstrate that the CVAE‐based inversion method captures the temporal variations in SZ metrics better than standard ordinary kriging. With the improved SZ metrics, the PB model more accurately reproduces the salient patterns of the multistage mass‐discharge profiles and associated uncertainty. This approach can be used to provide valuable input for risk‐based decision making in remediation applications. We propose a framework to estimate mass discharge from complex dense nonaqueous phase liquid (DNAPL) source zones (SZ) using sparse monitoring dataThe proposed framework outperforms standard kriging method in characterizing the SZ metrics controlling mass‐transfer kineticsCombining the improved SZ metrics with an upscaled model allows better reproduction of multistage DNAPL mass discharge We propose a framework to estimate mass discharge from complex dense nonaqueous phase liquid (DNAPL) source zones (SZ) using sparse monitoring data The proposed framework outperforms standard kriging method in characterizing the SZ metrics controlling mass‐transfer kinetics Combining the improved SZ metrics with an upscaled model allows better reproduction of multistage DNAPL mass discharge

Hydrological droughts are events of prolonged water scarcity and cause many devastating impacts. It is, therefore, extremely crucial to understand their spatiotemporal evolution to guide prevention and mitigation policies. The Gravity Recovery and Climate Experiment (GRACE, April 2002–June 2017) and GRACE Follow‐On (GRACE‐FO, June 2018 until the present) missions have been used to study large‐scale droughts of almost two decades. But characterizing droughts during the between missions gap period of 2017 and 2018 has not been well addressed and will be covered here. To bridge the gap, an innovative Bayesian convolutional neural network is developed to reconstruct the missing signals from hydroclimatic inputs. The reconstruction fields and existing signals are then used to explore regions that have experienced consecutive water storage deficits during the 2017–2018 gap. We found many regions of the northern midlatitudes exhibiting moderate to exceptional droughts in terms of water storage deficits, among which parts of Pakistan and Afghanistan, and Iberian Peninsula experienced exceptional droughts lasting for more than 1 year with the maximum deficits (−4.4 ± 0.8 and −7.2 ± 1.1 cm, respectively) being over 50% of the seasonal storage variations. Comparisons with climate indicators show that the identified droughts are predominantly caused by continuous below‐normal precipitation. The recovery process correlates generally well with the accumulation rate of precipitation surpluses (the correlation coefficient (R) can be up to 0.92). Besides, the reconstructed signals, which have R> 0.7 with the testing GRACE(‐FO) data in over 90% of the globe, reliably maintain the data continuity and therefore they are recommended for hydro‐climatological studies. The missing terrestrial water storage anomaly signals between the 2017–2018 Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On gap are reconstructed by Bayesian deep learningHydrological droughts overlapping with this gap period are characterized and quantifiedThe northern midlatitudes experienced continuous water storage deficits as a result of precipitation deficiency The missing terrestrial water storage anomaly signals between the 2017–2018 Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow‐On gap are reconstructed by Bayesian deep learning Hydrological droughts overlapping with this gap period are characterized and quantified The northern midlatitudes experienced continuous water storage deficits as a result of precipitation deficiency

Real‐time monitoring of the microbial activity in porous media faces considerable challenges because conventional analytical procedures are intrusive. Induced polarization has shown promises as a non‐intrusive monitoring approach for such processes. However, quantitative analysis of soil column experiments to show how IP‐based parameters can be related to the density of bacteria is still lacking. The evolution of bacterial density and the induced polarization was performed in soil‐packed column experiments using Pseudomonas aeruginosaO1. A quantitative relationship between the quadrature conductivity and the bacterial density was established and further applied to assess the bacterial growth and decay, and infer the Monod kinetic parameters. The Monod kinetic parameters inferred from the column experiments mimicking field conditions were different to those obtained from the batch experiments. Induced polarization is a non‐invasive geophysical method that is qualitatively sensitive to the presence of bacteria in porous media. The soil column was inoculated with a culture of the chosen bacterium. Bacterial growth and decay were monitored using time‐lapse induced polarization. Polarization was linearly related to the density of the bacteria in the soil columns. This finding was then used to non‐intrusively assess the key kinetic parameters controlling the fate of these bacteria in soil. Induced polarization can be effectively and non‐intrusively used to monitor microbial growth and decay processes in soil column experimentsA quantitative relationship between quadrature conductivity and bacterial density in soil columns is established to assess bacterial growth and decayMonod kinetic parameters inferred from the column experiments under field conditions are much lower than those from batch experiments Induced polarization can be effectively and non‐intrusively used to monitor microbial growth and decay processes in soil column experiments A quantitative relationship between quadrature conductivity and bacterial density in soil columns is established to assess bacterial growth and decay Monod kinetic parameters inferred from the column experiments under field conditions are much lower than those from batch experiments

Background:Frankincense and myrrh (FM) are often used together to treat knee osteoarthritis (KOA). However, the underlying mechanism of its treatment of KOA remains unclear. Objective:To analyze the active components of FM through network pharmacology and in vitro experiments, and to explore its potential therapeutic mechanism in the treatment of KOA. Materials and methods:The protein mapping relationship between potential drug targets and disease targets was screened and constructed through the database. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using R software. Discovery Studio software was used to perform molecular docking. The active components of FM were identified using liquid chromatography–mass spectrometry (LC-MS). In addition, experimental verification was carried out by Cell Counting Kit-8 detection, Western blot, and immunofluorescence analysis. Results:Combining the results of network pharmacology and LC-MS, 31 active compounds and 94 target genes of FM were identified. The common genes of FM and KOA suggest that FM exerts anti-KOA effect by regulating genes such as Transcription factor Jun (JUN), Interleukin-6 (IL-6), Interleukin-1 beta (IL-1β), C-X-C motif chemokine ligand 8 (CXCL8), Transcription factor p65 (RELA), and Mitogen-activated protein kinase 1 (MAPK1). GO enrichment analysis showed that FM exerted therapeutic effects on KOA by regulating biological processes such as cell proliferation, cell migration, and apoptosis. In addition, KEGG enrichment analysis involved signaling pathways such as fluid shear stress, the TNF, PI3K-Akt, NF-κB, and MAPK. Consistently, in vivo experiments showed that FM inhibited IL-1β-induced MAPK activation and attenuated inflammation in mouse chondrocytes. Furthermore, FM inhibited IL-1β-induced phosphorylation of p65 and the process of p65 translocation from the cytoplasm into the nucleus. Conclusions:Our results provide conclusive evidence and deepen the current understanding of FM in the treatment of KOA and further support its clinical application.

Gambogic acid (GA) is a natural compound derived from Chinese herbs that has been approved by the Chinese Food and Drug Administration for clinical trials in cancer patients; however, its molecular targets have not been thoroughly studied. Here, we report that GA inhibits tumor proteasome activity, with potency comparable to bortezomib but much less toxicity. First, GA acts as a prodrug and only gains proteasome-inhibitory function after being metabolized by intracellular CYP2E1. Second, GA-induced proteasome inhibition is a prerequisite for its cytotoxicity and anticancer effect without off-targets. Finally, because expression of the CYP2E1 gene is very high in tumor tissues but low in many normal tissues, GA could therefore produce tissue-specific proteasome inhibition and tumor-specific toxicity, with clinical significance for designing novel strategies for cancer treatment.

Estrogen receptor (ER) α and β are ligand-activated nuclear transcription factors that mediate the effects of the steroid hormone 17β-estradiol. Tissue-selective ER modulators have been developed for the treatment of a variety of diseases, including osteoporosis and hormone-dependent breast cancer. Second- and third-generation selective ER modulators are in development, with the goal of reducing toxicity and improving tissue-selective efficacy. Novel tissue-selective and ERsubtype specific ligands may have the potential of providing a new paradigm for maintaining the health of women. The traditional cell-based screening assays for nuclear receptors require 16–18 h of incubation, which limits the assay miniaturization for ultra-high-throughput screening. We have developed a new cell-based ERα transactivation assay for the screening of ERα-specific antagonists with only 4 h of incubation time. The assay was optimized and used for a fully automated ultrahigh- throughput screen in 3,456-well nanoplate format. The screening throughput was 250,000–300,000 compounds per day, and a number of valuable leads were identified.

Microtubule-interfering agents are widely used in cancer chemotherapy, and prognostic results vary significantly from tumor to tumor, depending on the p53 status. In preliminary experiments, we compared the expression and phosphorylation profiles of more than 100 protein kinases and protein phosphatases in human colorectal carcinoma cell line HCT116 between p53+/+ and p53-/- cells in response to short term nocodazole treatment through application of Kinetworks immunoblotting screens. Among the proteins tracked, the regulation of the phosphorylation of c-Jun N-terminal kinase (JNK)1/2 at Thr-183/Tyr-185 was the major difference between p53+/+ and p53-/- cells. With the loss of the p53 gene, the levels of phosphorylation of Ser-63 of c-Jun and Thr-183/Tyr-185 of JNK1/2 in p53-/- cells did not increase as markedly as in p53+/+ cells in response to a 1-h treatment with nocodazole or other microtubule-disrupting drugs such as vinblastine and colchicine. Similar observations were also made in MCF-7 and A549 tumor cells, which were rendered p53-deficient by E6 oncoprotein expression. However, arsenate-induced JNK activation in p53-/- cells was preserved. Inhibition of p53 expression by its antisense oligonucleotide also attenuated nocodazole-induced JNK activation in p53+/+ cells. Surprisingly, cotransfection of p53+/+ cells with dominant negative mutants of JNK isoforms and treatment of p53+/+ cells with the JNK inhibitor SP600125 actually further enhanced apoptosis in p53+/+ cells by up to 2-fold in response to nocodazole. These findings indicate that inhibition of p53-mediated JNK1/2 activity in certain tumor cells could serve to enhance the apoptosis-inducing actions of cancer chemotherapeutic agents that disrupt mitotic spindle function.

We have identified a direct physical interaction between the stress signaling p38α MAP kinase and the mitogen-activated protein kinases ERK1 and ERK2 by affinity chromatography and coimmunoprecipitation studies. Phosphorylation and activation of p38α enhanced its interaction with ERK1/2, and this correlated with inhibition of ERK1/2 phosphotransferase activity. The loss of epidermal growth factor-induced activation and phosphorylation of ERK1/2 but not of their direct activator MEK1 in HeLa cells transfected with the p38α activator MKK6(E) indicated that activated p38α may sequester ERK1/2 and sterically block their phosphorylation by MEK1.

Detailed characterization of dense nonaqueous phase liquid (DNAPL) source zone architecture (SZA) is essential for designing efficient remediation strategies. However, it is difficult to characterize a highly irregular and localized SZA, because traditional drilling investigations provide limited information. With limited data, the estimation accuracy of traditional geostatistical methods is strongly affected by the parameterization of the prior description of the SZA. To improve characterization performance, we parameterized the DNAPL saturation field using a physics‐based approach. We trained a convolutional variational autoencoder (CVAE) using data from multiphase modeling that captures the physics of DNAPL infiltration. The trained CVAE network was used in SZA inversion to obtain an improved prior DNAPL saturation field, instead of the typical stationary prior covariances. We then integrated the CVAE network into an iterative ensemble smoother (ES), to formulate a joint inversion framework. To overcome difficulties from limited/sparse data, we incorporated hydrogeological and geophysical datasets in the proposed inversion framework. To evaluate the performance of our method, we conducted numerical experiments in a hypothetical heterogeneous aquifer with an intricate SZA. The results show that the CVAE was an effective and efficient parameterization method which can capture the DNAPL infiltration patterns better than a Gaussian prior. The improved prior, combined with multisource datasets, can result in better resolution, and overall improved SZA characterization. In contrast to the standard ES method, the proposed framework reconstructed the SZA more accurately. We also demonstrated that DNAPL depletion behavior and dissolved concentration profiles can be predicted accurately using the estimated SZA. Toxic organic contaminants, such as chlorinated solvents, often exist in the form of oily phases that do not mix with water and are denser than water. Such dense nonaqueous phase liquids (DNAPLs) represent a challenging environmental problem worldwide. To develop efficient remediation strategies, the quantity and morphology of the DNAPL in the subsurface needs to be identified. However, state‐of‐the‐art identification methods cannot reproduce accurate and physically realistic patterns of DNAPL source zone architecture (SZA). In this work, we developed a deep‐learning strategy that can satisfactorily capture the physical patterns of the DNAPL SZA by learning these patterns from training samples obtained from multiphase modeling. This parameterization strategy was further integrated with an ensemble‐based inversion method to estimate the SZA by incorporating multisource data sets. The proposed framework produced more accurate estimates of both the quantity and distribution of DNAPL than the results from traditional inversion methods that utilized a purely statistical model to represent the SZA when using data to finetune the characterization. A variational autoencoder (VAE) is proposed for parameterizing complex dense nonaqueous phase liquid (DNAPL) source zones, preserving their nonstationary spatial characteristicsIntegrating VAE and ensemble smoother (ES) outperforms the standard ES in identifying both the residual and pooled DNAPLCombining hydrogeological and geophysical data sets can improve the characterization of the nonstationary DNAPL distribution A variational autoencoder (VAE) is proposed for parameterizing complex dense nonaqueous phase liquid (DNAPL) source zones, preserving their nonstationary spatial characteristics Integrating VAE and ensemble smoother (ES) outperforms the standard ES in identifying both the residual and pooled DNAPL Combining hydrogeological and geophysical data sets can improve the characterization of the nonstationary DNAPL distribution

Objective To assess the distinguishing features of aseptic meningitis (AM) in patients with Kawasaki disease (KD) compared with bacterial meningitis (BM) patients.Methods Thirty-eight patients with KD and 126 patients with BM were retrospectively investigated. The following clinical manifestations and laboratory parameters were compared between the two groups: duration of fever before lumbar puncture, conjunctival injection, oral cavity changes, rash, cervical lymphadenopathy and extremity changes, vomiting, front fontanel bulging, neck stiffness, leukocyte number, hemoglobin level, platelet number, C-reactive protein level, cerebrospinal fluid (CSF) content, liver enzyme level, and urinalysis.Results Vomiting and neck stiffness were more prevalent in patients with BM. KD patients with AM showed elevated blood leukocyte numbers and C-reactive protein levels in the early febrile stage. CSF glucose was significantly lower in patients with BM compared with KD patients with AM. Receiver operating characteristic curve analysis showed that the optimal cutoff value of CSF glucose for discrimination of BM and AM/KD was 2.945 mmol/L, with a sensitivity of 84.2% and a specificity of 71.4%.Conclusions Detailed investigations of clinical manifestation and laboratory parameters are necessary to distinguish AM and BM in patients with KD. Decreased CSF glucose is a potential indicator of BM.

Objective The molecular mechanism of Salviae Miltiorrhizae Radix et Rhizoma (SMRR) in the treatment of knee osteoarthritis (KOA) was analyzed based on network pharmacology.Methods Active components and potential targets of SMRR were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. KOA targets were obtained from the OMIM, DisGeNET, DrugBank, PharmGKB, and GeneCards Databases. The potential targets of SMRR in the treatment of KOA were identified by the Venn diagram. A protein-protein interaction network was generated with the STRING database. Visualization of the interactions in a potential pharmacodynamic component-target network was accomplished with Cytoscape software. The Database for Annotation, Visualization, and Integrated Discovery database and R software were used for Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway annotation analyses of common targets. Molecular docking of the potential leading components, as determined by efficacy with the core target molecules, was performed with Discovery Studio.Results Fifty-seven potential pharmacodynamic components and 58 potential targets of SMRR in the treatment of KOA were found. Bioinformatics analyses showed that the interleukin (IL)-17, hypoxia-inducible factor-1 (HIF-1), and tumor necrosis factor (TNF) signaling pathways, as well as the advanced glycation end product-receptor for advanced glycation end product signaling pathway in cases of diabetic complications, are related to the molecular mechanism of SMRR in the treatment of KOA. Molecular docking results showed that luteolin, tanshinone IIA, cryptotanshinone, and other components of SMRR had a strong affinity for MYC, signal transducer and activator of transcription 3, caspase-3 (CASP3), JUN, cyclin D1, prostaglandin endoperoxide synthase 2 (PTGS2), epidermal growth factor receptor (EGFR), mitogen-activated protein kinase 1 (MAPK1), protein kinase B, vascular endothelial growth factor A, and other targets.Conclusion SMRR indirectly regulates IL-17, HIF-1, TNF, and other signal transduction pathways by regulating the expression of proteins, including PTGS2, MAPK1, EGFR, and CASP3, thus playing a role in promoting chondrocyte proliferation, improving microcirculation, eliminating free radicals, and inhibiting inflammatory factors.

High‐resolution characterization of hydraulic properties and dense nonaqueous phase liquid (DNAPL) contaminant source is crucial to develop efficient remediation strategies. However, DNAPL characterization suffers from a limited number of borehole data in the field, resulting in a low‐resolution estimation. Moreover, high‐resolution DNAPL characterization requires a large number of unknowns to be estimated, presenting a computational bottleneck. In this paper, a low‐cost geophysical approach, the self‐potential method, is used as additional information for hydraulic properties characterization. Joint inversion of hydraulic head and self‐potential measurements is proposed to improve hydraulic conductivity estimation, which is then used to characterize the DNAPL saturation distribution by inverting partitioning tracer measurements. The computational barrier is overcome by (a) solving the inversion by the principal component geostatistical approach, in which the covariance matrix is replaced by a low‐rank approximation, thus reducing the number of forward model runs; (b) using temporal moments of concentrations instead of individual concentration data points for faster forward simulations. To assess the ability of the proposed approach, numerical experiments are conducted in a 3‐D aquifer with 104unknown hydraulic conductivities and DNAPL saturations. Results show that with realistic DNAPL sources and a limited number of hydraulic heads, the traditional hydraulic/partitioning tracer tomography roughly reconstructs subsurface heterogeneity but fails to resolve the DNAPL distribution. By adding self‐potential data, the error is reduced by 24% in hydraulic conductivity estimation and 68% in DNAPL saturation characterization. The proposed sequential inversion framework utilizes the complementary information from multi‐source hydrogeophysical data sets, and can provide high‐resolution characterizations for realistic DNAPL sources. The low‐cost self‐potential (SP) method improves the results of effective hydraulic conductivity (Keff) estimationUsing a sequential strategy, the improved Keffestimation from SP can increase the accuracy of DNAPL mappingThe sequential inversion framework helps in reducing significantly the computational effort for DNAPL inversion

Inverse modeling for the estimation of non‐Gaussian hydraulic conductivity fields in subsurface flow and solute transport models remains a challenging problem. This is mainly due to the non‐Gaussian property, the nonlinear physics, and the fact that many repeated evaluations of the forward model are often required. In this study, we develop a convolutional adversarial autoencoder (CAAE) to parameterize non‐Gaussian conductivity fields with heterogeneous conductivity within each facies using a low‐dimensional latent representation. In addition, a deep residual dense convolutional network (DRDCN) is proposed for surrogate modeling of forward models with high‐dimensional and highly complex mappings. The two networks are both based on a multilevel residual learning architecture called residual‐in‐residual dense block. The multilevel residual learning strategy and the dense connection structure ease the training of deep networks, enabling us to efficiently build deeper networks that have an essentially increased capacity for approximating mappings of very high complexity. The CAAE and DRDCN networks are incorporated into an iterative ensemble smoother to formulate an inversion framework. The numerical experiments performed using 2‐D and 3‐D solute transport models illustrate the performance of the integrated method. The obtained results indicate that the CAAE is a robust parameterization method for non‐Gaussian conductivity fields with different heterogeneity patterns. The DRDCN is able to obtain accurate approximations of the forward models with high‐dimensional and highly complex mappings using relatively limited training data. The CAAE and DRDCN methods together significantly reduce the computation time required to achieve accurate inversion results. A convolutional adversarial autoencoder is developed to parameterize non‐Gaussian conductivity fields with multimodal distributionsA deep residual dense convolutional network is introduced as a surrogate of the forward physics‐based modelThe integrated method is tested with inverse problems for the estimation of non‐Gaussian conductivities in solute transport modeling

MicroRNAs play critical roles in the occurrence and progression in various cancers including colorectal cancer. Here, we found that microRNA-30a expression was significantly downregulated in colorectal cancer tissues compared to adjacent noncancerous tissues, and the suppression levels of microRNA-30a were significantly associated with tumor differentiation and lymph node metastasis. We also discovered that the expression level of microRNA-30a was inversely proportional to the invasive potential of several colorectal cancer cell lines. Moreover, overexpression of microRNA-30a in colorectal cancer cells inhibited activity of cell migration and invasion. Luciferase reporter assay confirmed metadherin could be a direct target of microRNA-30a, as the overexpression of microRNA-30a decreased metadherin expression at both the protein and messenger RNA levels. Furthermore, the knockdown of metadherin expression in SW620 significantly decreased cell metastasis and invasion. The upregulation of metadherin at the protein level negatively correlated with the expression of microRNA-30a in colorectal cancer tissues, and this upregulation could partially attenuate the effect induced by microRNA-30a. These findings indicate that microRNA-30a may act as a tumor suppressor in colorectal cancer and that microRNA-30a represses cell migration and invasion by decreasing metadherin, highlighting the therapeutic potential of microRNA-30a and metadherin in colorectal cancer treatment.