Search Results (10)

Background/Objectives: Silicosis remains a major occupational health concern worldwide and is characterized by notable clinical heterogeneity in terms of disease progression and complications. However, the underlying metabolic mechanisms contributing to this heterogeneity remain poorly understood. Methods: We conducted a case–control study involving 156 silicosis patients and 132 silica-exposed controls. The plasma samples were analyzed via untargeted metabolomics based on liquid chromatography–mass spectrometry (LC-MS/MS). To explore disease subtypes and potential biomarkers, we applied non-negative matrix factorization (NMF) clustering, weighted gene co-expression network analysis (WGCNA), and machine learning approaches. Results: A total of 860 differentially abundant metabolites, including elevated pathogen-associated compounds, were identified in silicosis patients. Unsupervised NMF clustering revealed two distinct metabolic subtypes with different clinical features. Patients in the NMF2 subgroup had a 5.3-fold greater risk of pulmonary infections (p = 0.026) than those in the NMF1 subgroup. Metabolomic analysis revealed that NMF2 was enriched in arachidonic acid and unsaturated fatty acid metabolism pathways, with prominent LysoPC accumulation, suggesting inflammation-related lipid peroxidation. In contrast, NMF1 was characterized by increased spermidine biosynthesis and urea cycle activity, along with suppressed saturated fatty acid metabolism and reduced LysoPC processing, potentially affecting membrane integrity and promoting fibrosis. A machine learning-derived dual-metabolite panel, tyrosocholic acid and PI (20:4/0:0), achieved AUC values above 0.85 for both silicosis detection and subtype classification. Conclusions: These findings highlight metabolic heterogeneity in silicosis and suggest clinically relevant subtypes, providing a foundation for improved stratification, early detection, and targeted interventions. Full article

Data assimilation for unconfined seepage analysis has faced significant challenges due to hybrid causes, such as sparse measurements, heterogeneity of porous media, and computationally expensive forward models. To address these bottlenecks, this paper introduces a physics-informed neural network (PINN) model to resolve the data assimilation problem for seepage analysis of unsaturated earth–rockfill dams. This strategy offers a solution that decreases the reliance on numerical models and enables an accurate and efficient prediction of seepage parameters for complex models in the case of sparse observational data. For the first attempt in this study, the observed values are obtained by random sampling of numerical solutions, which are then contributed to the synchronous constraints in the loss function by informing both the seepage control equations and boundary conditions. To minimize the effects of sharp gradient shifts in seepage parameters within the research domain, a residual adaptive refinement (RAR) constraint is introduced to strategically allocate training points around positions with significant residuals in partial differential equations (PDEs), which could facilitate enhancing the prediction accuracy. The model’s effectiveness and precision are evaluated by analyzing the proposed strategy against the numerical solutions. The results indicate that even with limited sparse data, the PINN model has great potential to predict seepage data and identify complex structures and anomalies inside the dam. By incorporating coupling constraints, the validity of our PINN model could lead to theoretically viable applications of hydrogeophysical inversion or multi-parameter seepage inversion. The results show that the proposed framework can predict the seepage parameters for the entire research domain with only a small amount of observation data. Furthermore, with a small amount of observation data, PINNs are able to obtain more accurate results than purely data-driven DNNs. Full article

The computational cost of approximating the Richards equation for water flow in unsaturated porous media is a major challenge, especially for tasks that require repetitive simulations. Data-driven modeling offers a faster and more efficient way to estimate soil moisture dynamics, significantly reducing computational costs. Typically, data-driven models use one-dimensional vectors to represent soil moisture at specific points or as a time series. However, an alternative approach is to use images that capture the distribution of porous media characteristics as input, allowing for the estimation of the two-dimensional soil moisture distribution using a single model. This approach, known as image-to-image regression, provides a more explicit consideration of heterogeneity in the porous domain but faces challenges due to increased input–output dimensionality. Deep neural networks (DNNs) provide a solution to tackle the challenge of high dimensionality. Particularly, encoder–decoder convolutional neural networks (ED-CNNs) are highly suitable for addressing this problem. In this study, we aim to assess the precision of ED-CNNs in predicting soil moisture distribution based on porous media characteristics and also investigate their effectiveness as an optimizer for inverse modeling. The study introduces several novelties, including the application of ED-CNNs to forward and inverse modeling of water flow in unsaturated porous media, performance evaluation using numerical model-generated and laboratory experimental data, and the incorporation of image stacking to account for transient moisture distribution. A drainage experiment conducted on a sandbox flow tank filled with monodisperse quartz sand was employed as the test case. Monte Carlo simulation with a numerical model was employed to generate data for training and validation of the ED-CNN. Additionally, the ED-CNN optimizer was validated using images obtained through non-intrusive photographic imaging. The results show that the developed ED-CNN model provides accurate approximations, addressing the high-dimensionality problem of image-to-image regression. The data-driven model predicted soil moisture with an R² score of over 91%, while the ED-CNN optimizer achieved an R² score of over 89%. The study highlights the potential of ED-CNNs as reliable and efficient tools for both forward and inverse modeling in the analysis of unsaturated flow. Full article

The crystalline basement aquifer of the Pra Basin in Ghana is essential to the water supply systems of the region. This region is experiencing the ongoing pollution of major river networks from illegal mining activities. Water management is difficult due to the limited knowledge of hydrochemical controls on the groundwater. This study investigates its evolution based on analyses from a previous groundwater sampling campaign and mineralogical investigation of outcrops. The dominant reactions driving the average groundwater composition were identified by means of a combinatorial inverse modelling approach under the hypothesis of local thermodynamical equilibrium. The weathering of silicate minerals, including albite, anorthite, plagioclase, K-feldspar, and chalcedony, explains the observed median groundwater composition in the transition and discharge zones. Additional site-specific hypotheses were needed to match the observed composition of the main recharge area, including equilibration with carbon dioxide, kaolinite, and hematite in the soil and unsaturated zones, respectively, and the degradation of organic matter controlling the sulfate/sulfide content, thus pointing towards kinetic effects during water–rock interactions in this zone. Even though an averaged water composition was used, the inverse models can “bridge” the knowledge gap on the large basin scale to come up with quite distinct “best” mineral assemblages that explain observed field conditions. This study provides a conceptual framework of the hydrogeochemical evolution for managing groundwater resources in the Pra Basin and presents modelling techniques that can be applied to similar regions with comparable levels of heterogeneity in water chemistry and limited knowledge of aquifer mineralogy. The combinatorial inverse model approach offers enhanced flexibility by systematically generating all plausible combinations of mineral assemblages from a given pool of mineral phases, thereby allowing for a comprehensive exploration of the reactions driving the chemical evolution of the groundwater. Full article

The origin of life and native tissue development are dependent on the heterogeneity of pluripotent stem cells. Bone marrow mesenchymal stem cells (BMMSCs) are located in a complicated niche with variable matrix stiffnesses, resulting in divergent stem cell fates. However, how stiffness drives stem cell fate remains unknown. For this study, we performed whole-gene transcriptomics and precise untargeted metabolomics sequencing to elucidate the complex interaction network of stem cell transcriptional and metabolic signals in extracellular matrices (ECMs) with different stiffnesses, and we propose a potential mechanism involved in stem cell fate decision. In a stiff (39~45 kPa) ECM, biosynthesis of aminoacyl-tRNA was up-regulated, and increased osteogenesis was also observed. In a soft (7~10 kPa) ECM, biosynthesis of unsaturated fatty acids and deposition of glycosaminoglycans were increased, accompanied by enhanced adipogenic/chondrogenic differentiation of BMMSCs. In addition, a panel of genes responding to the stiffness of the ECM were validated in vitro, mapping out the key signaling network that regulates stem cells’ fate decisions. This finding of “stiffness-dependent manipulation of stem cell fate” provides a novel molecular biological basis for development of potential therapeutic targets within tissue engineering, from both a cellular metabolic and a biomechanical perspective. Full article

Groundwater evapotranspiration (ET_g) is an important component of the hydrological cycle in water-scarce regions and is important for local ecosystems and agricultural irrigation management. However, accurate estimation of ET_g is not easy due to uncertainties in climatic conditions, vegetation parameters, and the hydrological parameters of the unsaturated zone and aquifers. The current methods for calculating ET_g mainly include the WTF method and the numerical groundwater model. The WTF method often requires data supplementation from the numerical unsaturated model to reduce uncertainty; in addition, it relies on point-monitoring data and cannot solve the spatial heterogeneity of ET_g. The ET_g calculation module of the numerical groundwater model is set up too simply and ignores the influence from the unsaturated zone and surface cover. Subsequent research breakthroughs should focus on the improvement of WTF calculation theory and the setting up of an aquifer water-table fluctuation monitoring network. The numerical groundwater model should couple the surface remote sensing data with the unsaturated zone model to improve the accuracy of ET_g calculation. Full article

In view of the industrial importance of high-grade unsaturated ether (UE) and the inconvenience of acquiring the compound, herein, a series of low-cost Al-Ni-P catalysts in robust AlPO₄/Ni₂P structure possessing novel bi-functional catalytic features (hydrogenation activation and acid catalysis) were innovated, and testified to be efficient for directly synthesizing UE with a superior yield up to 97% from the selective hydrogenation coupling carbonyl of unsaturated aldehyde (cinnamaldehyde or citral) with C1–C5 primary or secondary alcohol under 0.1 MPa H₂ and 393 K. The integrated advantages of high efficiency, green manner and convenient operation of the present heterogeneous catalytic system gave the system potential for feasibly harvesting high-grade unsaturated ether in related fine chemical synthesis networks. Full article

This paper proposes a medium access control (MAC) protocol based on deep reinforcement learning (DRL), i.e., multi-channel transmit deep-reinforcement learning multi-channel access (MCT-DLMA) in heterogeneous wireless networks (HetNets). The work concerns practical unsaturated channel traffic that arrives following a Poisson distribution instead of saturated traffic that arrives before.By learning the access mode from historical information, MCT-DLMA can well fill the spectrum holes in the communication of existing users. In particular, it enables the cognitive user to multi-channel transmit at a time, e.g., via the multi-carrier technology. Thus, the spectrum resource can be fully utilized, and the sum throughput of the HetNet is maximized. Simulation results show that the proposed algorithm provides a much higher throughput than the conventional schemes, i.e., the whittle index policy and the DLMA algorithms for both the saturated and unsaturated traffic, respectively. In addition, it also achieves a near-optimal result in dynamic environments with changing primary users, which proves the enhanced robustness to time-varying communications. Full article

Several studies currently strive to improve the spatial resolution of coarse scale high temporal resolution global soil moisture products of SMOS, SMAP, and ASCAT. Soil texture heterogeneity is known to be one of the main sources of soil moisture spatial variability. With the recent development of high resolution maps of basic soil properties such as soil texture and bulk density, relevant information to estimate soil moisture variability within a satellite product grid cell is available. We use this information for the prediction of the sub-grid soil moisture variability for each SMOS, SMAP, and ASCAT grid cell. The approach is based on a method that predicts the soil moisture standard deviation as a function of the mean soil moisture based on soil texture information. It is a closed-form expression using stochastic analysis of 1D unsaturated gravitational flow in an infinitely long vertical profile based on the Mualem-van Genuchten model and first-order Taylor expansions. We provide a look-up table that indicates the soil moisture standard deviation for any given soil moisture mean, available at https://doi.org/10.1594/PANGAEA.878889. The resulting data set helps identify adequate regions to validate coarse scale soil moisture products by providing a measure of representativeness of small-scale measurements for the coarse grid cell. Moreover, it contains important information for downscaling coarse soil moisture observations of the SMOS, SMAP, and ASCAT missions. In this study, we present a simple application of the estimated sub-grid soil moisture heterogeneity scaling down SMAP soil moisture to 1 km resolution. Validation results in the TERENO and REMEDHUS soil moisture monitoring networks in Germany and Spain, respectively, indicate a similar or slightly improved accuracy for downscaled and original SMAP soil moisture in the time domain for the year 2016, but with a much higher spatial resolution. Full article

Studies of the IEEE 802.15.4 Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) scheme have been received considerable attention recently, with most of these studies focusing on homogeneous or saturated traffic. Two novel transmission schemes—OSTS/BSTS (One Service a Time Scheme/Bulk Service a Time Scheme)—are proposed in this paper to improve the behaviors of time-critical buffered networks with heterogeneous unsaturated traffic. First, we propose a model which contains two modified semi-Markov chains and a macro-Markov chain combined with the theory of M/G/1/K queues to evaluate the characteristics of these two improved CSMA/CA schemes, in which traffic arrivals and accessing packets are bestowed with non-preemptive priority over each other, instead of prioritization. Then, throughput, packet delay and energy consumption of unsaturated, unacknowledged IEEE 802.15.4 beacon-enabled networks are predicted based on the overall point of view which takes the dependent interactions of different types of nodes into account. Moreover, performance comparisons of these two schemes with other non-priority schemes are also proposed. Analysis and simulation results show that delay and fairness of our schemes are superior to those of other schemes, while throughput and energy efficiency are superior to others in more heterogeneous situations. Comprehensive simulations demonstrate that the analysis results of these models match well with the simulation results. Full article